78581 Time to shift gears Accelerating growth and poverty reduction in the new Kenya TABLE OF CONTENTS ABBREVIATIONS AND ACRONYMS i FOREWORD ii ACKNOWLEDGEMENTS iii MAIN MESSAGES AND KEY RECOMMENDATIONS iv EXECUTIVE SUMMARY v THE STATE OF KENYA’S ECONOMY 1 1. Economic performance 2 1.1 A resilient economy in times of adversity 2 1.2 Kenya’s fiscal prudency is paying off 6 1.3 Monetary conditions have eased but policy must keep an eye on inflation while 12 supporting growth 1.4 The External Sector poses challenges and risks to Kenya’s growth prospects 15 2. Growth Outlook for 2013-2014 24 2.1 Growth Prospects 24 2.2 Risks to outlook 26 2.3 Important priorities for the near and medium term 27 SPECIAL FOCUS: POVERTY 32 3. Poverty 32 3.1 Poverty in Kenya 32 3.2 How has income-poverty changed? 38 3.3 How have broader measures of welfare changed? 41 3.4 Making public spending work for the poor 47 3.5 What will it take to make poverty history? 52 3.6 Poverty reduction: the way forward 54 ANNEXES Annex 1: Macroeconomic environment 58 Annex 2: GDP Growth Rates 2008-2012 Kenya SSA EAC 58 Annex 3: Kenya annual GDP 59 Annex 4: Quartely growth rates (percent) 60 Annex 5: Inflation 61 Annex 6: Tea production and exports 62 Annex 7: Coffee production and exports 63 Annex 8: Horticulture exports 64 Annex 9: Local electricity generation by source (Million KWh) 65 Annex 10: Soft drinks and sugar production 66 Annex 11: Tourism arrivals 67 Annex 12: New vehicle registration 68 Annex 13: Exchange rate 69 Annex 14: Interest rates 70 Annex 15: Credit to private sector 71 Annex 16: Money aggregate 72 Annex 17: Mobile payments 73 Annex 18: Nairobi stock exchange (20 share index) and the dow jones (New York) 74 Annex 19: Nominal and real exchange rate 75 Annex 20: Fiscal position 76 Annex 21: 12-Months Cumulative Balance of Payments In millions of US dollars 77 Annex 22: Growth Outlook 79 Annex 23: Maize prices in Kenya 80 Annex 24: Methods 82 Annex 25: Illustrations of distributional impact of inequality on the distribution of consumption 84 Annex 26. National accounts based predictions of poverty using $1.25 dollar per day poverty line 85 Annex 27. Trends in household living standard indicators between 1989 & 2009 86 List of Figures Figure 1: Kenya is lagging its peers v Figure 2: Has growth and inequality driven poverty down? vii Figure 3: Focusing on poor regions …or poor people? viii Figure 1.1: Growth picked up during the second half of 2012 2 Figure 1.2: Kenya is lagging behind SSA 3 Figure 1.3: Growth in 2012 was broad based 4 Figure 1.4: Hydro generation rebounded in 2012 4 Figure 1.5: Growth in services declined in all sectors including tourism except public administration 4 Figure 1.6: Consumption continues to be the key driver of Kenya’s growth offsetting net 5 export weakness Figure 1.7: Inflationary pressures have come under control 6 Figure 1.8: Fiscal position remains strong despite deterioration of the overall balance 6 Figure 1.9: Kenya’s public debt declined in 2012 7 Figure 1.10: Yield curve movements in the last 9 months reflected political developments 7 Figure 1.11: Principle sources of government revenue have remained the same over the years 8 Figure 1.12: Budget Cuts absorbed by recurrent spending while development spending was increased 9 Figure 1.13: Implementation of the budget remains a major challenge 9 Figure 1.14: Per capita allocation for counties shows wide variations 10 Figure 1.15: Monetary aggregates started increasing in response to policy easing in 12 the second half of 2012 Figure 1.16: Short term rates have responded to monetary policy which seems effective 12 Figure 1.17: Long term rates have remained high but are declining with monetary easing 13 Figure 1.18: Commercial banks pricing behavior of loans seems to vary across categories of borrowers 13 Figure 1.19: Commercial banks offered high deposit rates for term deposits to attract more liquidity 14 Figure 1.20: Tight monetary policy constrained credit to all sectors of the economy 14 Figure 1.21: Kenya’s equities are recovering strongly 15 Figure 1.22: A wider current account deficit is being financed by short term flows 16 Figure 1.23: Non-oil imports mainly capital imports increased significantly in 2012 while 17 oil imports remained flat Figure 1.24: FDI inflows into Kenya remains low compared to its peers in the region while 17 Short term flows have increased significantly Figure 1.25: Short term flows has become a significant factor in the balance of payment 18 Figure 1.26: The exchange rate stabilized in 2012 but has depreciated at the rate of 1-4 percent 22 per year in nominal terms in the last 10 years against the major currencies Figure 1.27: Kenya competitiveness continues to be eroded 22 Figure 1.28: Remittances have risen sharply in the last few years 23 Figure 2.1: A pickup in growth in 2013-14 24 Figure 2.2: Monetary policy space is available to support growth 25 Figure 2.3: A close relationship between inflation and credit to private sector 26 Figure 2.4: Savings and GNI per capita (2011) 28 Figure 2.5: Savings and investment, as at GDP (2011) 28 Figure 3.1: Kenya needs to ramp up poverty monitoring 32 Figure 3.2: The North and north-eastern, arid and semi-arid regions are the poorest regions in Kenya 35 Figure 3.3: The poor are concentrated where land is most fertile 36 Figure 3.4: Kenyans associate poverty with lack of food and money 37 Figure 3.5: Kenya’s welfare indicators in an international perspective 38 Figure 3.6: A decade of poverty reduction in Sub-Saharan Africa 40 Figure 3.7: The Gini-coefficient in Kenya and the region 41 Figure 3.8: Did strong growth drive poverty rates down? Depends how you think inequality 42 has changed Figure 3.9: More evidence that poverty has declined 43 Figure 3.10: The evolution of household characteristics 44 Figure 3.11: The geographic distribution of hardship in Kenya 46 Figure 3.12: Getting services can be difficult, especially for the poorest 48 Figure 3.13: Nilihonga (“I paid a bribe� in Swahili) 48 Figure 3.14: Poor facilities and overcrowding are the most common problems associated with schools 49 Figure 3.15: Teacher absenteeism: At school but not teaching 49 Figure 3.16: Long waits and lack of medicines are the most common problems associated 50 with health facilities Figure 3.17: Reaching the first Millennium Development Goal is unlikely 52 Figure 3.18: What Will it Take for Kenya to Reduce Extreme Poverty to 3 Percent by 2030? 53 Figure 3.19: Pillars of Social Protection in Kenya 54 List of Tables Table 1: Kenya: A country of contrasts in service delivery ix Table 1.1: Kenya’s revenue mobilization fully catering for its recurrent expenditure and 8 part of development Table 1.2: Kenya’s top exports and imports by broad functional category 19 Table 1.3: Selected Kenya’s Trading Partners 20 Table 2.1: Macroeconomic Indicators 2008-2014 24 Table 3.1: Looking back - patterns of poverty in 2005 33 Table 3.2: Data sources with information on household welfare 41 List of Boxes Box 1.1: Why is Kenya underperforming? 3 Box 1.2: Macroeconomic implications of devolution 11 Box 1.3: Could the deprecation of the Kenyan shilling be beneficial to Kenya’s economy? 21 Box 2.1: Higher savings for faster growth 28 Box 3.1: Kenya’s poverty line 34 Box 3.2: A small country with big declines in poverty 39 Box 3.3: Know your Gini! 41 Box 3.4: MaisIguais (“More Equal� in Portuguese) 45 Box 3.5: Give them a chance 47 Box 3.6: Turbocharging Poverty Reduction: The Case of Rwanda 53 Box 3.7: Strengthening Social Protection in Kenya 54 ABBREVIATIONS AND ACRONYMS BPS Budget Policy Statement CBK Central Bank of Kenya CBR Central Bank Rate CDF Constituency Development Fund CIP Crop Intensification Program CPI Consumer Price Index CRR Cash Reserve Ratio DHS Demographic and Health Survey EU European Union FDI Foreign Direct Investment GDP Gross Domestic Product GNI Gross National Income HFC Health facility committee HSSF Health Sector Services Fund HOI Human Opportunity Index IBEC Intergovernmental Budget and Economic Council ILO International Labour Organization IMF International Monetary Fund KES Kenya Shillings KEU Kenya Economic Update KIHBS Kenya Integrated Household Budget Survey KNBS Kenya National Bureau of Statistics KPLC Kenya Power and Lighting Corporation KRA Kenya Revenue Authority LATF Local Authorities Transfer Fund LIC Low Income Country LWH Land Husbandry, Water Harvesting and Hillside MIC Middle Income Country MPI Multi-Dimensional Poverty Index (MPI) MRC Mombasa Republican Council MTP Medium Term Plan NPLs Non-performing loans NSE Nairobi Stock Exchange NSPP National Social Protection Policy PFM Public Finance Management REER Real effective exchange rate SSA Sub-Saharan Africa SRC Salaries and Remunerations Commission UK United Kingdom UNCTAD United Nations Conference on Trade and Development UNDP United Nations Development Programme VAT Value Added Tax June 2013 | Edition No. 8 i FOREWORD I t is my pleasure to present to you the eighth edition of the World Bank’s Kenya Economic Update. Since the start of 2013, Kenyans have witnessed historic times. The successful elections in March and the peaceful transition of power in April, ushered in a new era of political leadership, which will guide the implementation of Kenya’s ambitious program of devolution. The report has three main messages. First, the economy is expected to achieve higher growth targets in 2013 (5.7 percent) and 2014 (6 percent) over what it achieved in 2012 (4.6 percent), as a result of the smooth election process. However, the government will need to make a concerted effort, if it wishes to approach the 10 percent annual growth rate foreseen in Vision 2030. The report’s second message emphasizes on the steps that the government needs to take to create an enabling framework for significant private sector-led growth. The Government needs to continue to invest in infrastructure, to increase domestic energy production, to address the other bottlenecks that affect the cost of doing business, and to continue following sound monetary and fiscal policies. Finally, the report’s third message focuses on the poverty situation in Kenya, noting progress made since 2005, when an estimated 47 percent of the population lived below the poverty line, to the present, where poverty estimates range between 34 and 42 percent, the imprecision resulting from the lack of any recent survey data. The report notes the spatial dimension of poverty, and the poor tend to live in the arid and semi-arid regions in the north and north east. It concludes with thoughts about a poverty reduction strategy, which would emphasize on job creation, enhanced productivity of smallholder farms, strengthening and expanding cash transfer programs, targeted public spending programs to provide quality education to the rural poor, and improved poverty monitoring, so that the government can rapidly see which activities have the greatest impacts on improving the lives of the poor. The World Bank remains committed to helping Kenya as it launches a new political administration that will have the challenging task of implementing a devolved form of government. The World Bank’s series of Economic Updates, which we publish in a new edition every six months, have become our leading vehicle to analyze development trends in Kenya, and to contribute to the implementation of the Bank’s strategy for sub-Saharan Africa, which puts a special emphasis on knowledge and partnerships. With these reports, we aim to support all those who want to improve economic management in Kenya. As in the past, we are proud to have worked with many Kenyan economic stakeholders during the preparation of this report. We hope that you too will join us in debating policy issues that are topical in Kenya today, and in making your contribution to helping Kenya to grow, to achieve a permanent reduction in poverty, and to bring shared prosperity to all Kenyans. Diarietou Gaye Country Director for Kenya World Bank ii June 2013 | Edition No. 8 ACKNOWLEDGEMENTS T his Eighth edition of the Kenya Economic Update was prepared by a team led by John Randa and Paul Gubbins together with BorkoHandjiski and supervised by Wolfgang Fengler. The core team consisted of Tom Bundervoet, Roger Sullivan, Allen Denis, Angelique Umutesi, Geoff Handley, Kathy Whimp, Lucy Wariara and Sophie Rabuku. The team acknowledges contributions from Robert Waiharo. The report benefitted from insights of several peer reviewers including Waly Wane, Lazar Sestovic, and Prof. Terry Ryan. The team also received guidance from Pablo Fajnzylber, Diarietou Gaye, Thomas O’Brien, Gabriel Demombynes and Andrew Dabalen. Partnership with key Kenyan policy makers was instrumental in the production of this report. On May 30, 2013, a draft of the report was presented at the Quarterly Economic Roundtable. The meeting was attended by senior officials from the National Treasury, Ministry of Devolution and Planning, the Central Bank of Kenya, the Kenya National Bureau of Statistics, the Kenya Revenue Authority, Kenya Institute of Public Policy Research and Analysis, the International Monetary Fund, and the National Economic and Social Council. June 2013 | Edition No. 8 iii MAIN MESSAGES AND KEY RECOMMENDATIONS Main Messages • Kenya’s economy is still operating below its potential. However, given the domestic and global environment, growth was satisfactory in 2012. After a peaceful election and transition in 2013, growth is projected to rise to 5.7 percent in 2013 and 6.0 percent in 2014, supported by lower interest rates and higher investment growth. • The economy is still vulnerable to external shocks, which can erode the significant gains it has achieved. This external vulnerability can be reduced by increasing both domestic and foreign savings. Structural reforms that improve the business environment would incentivize more FDI to flow to Kenya, and increase the rate of growth and savings. These reforms must involve tax and expenditure measures that increase both savings and investment so as to allow Kenya to take advantage of low labor costs, and its coastal location to expand manufacturing exports. • Poverty has likely declined. Kenya’s poverty level is estimated to have declined from 47 percent in 2005, to between 34 to 42 percent today (imprecise estimates due to the fact that the last household survey was conducted in 2005-06). Kenya needs to undertake a new survey to update poverty estimates, and inform government’s poverty reduction strategies. • Kenya remains a country of contrasts, especially in service delivery. On average, Kenyans are healthier, more educated, and receive better infrastructure services than they did a decade ago. At the same time, a large fraction of the population continues to live with sub-standard access to water, sanitation and energy. Key Recommendations to sustain the growth momentum • For the medium term, Kenya needs to boost productivity and regain its competitiveness. To maintain high growth rates, Kenya needs to continue investing in infrastructure and human capital, improve the business and regulatory environment, and diversify exports. The challenge for Kenya is to engineer policies to boost productivity growth and foster job creation, i.e. reinvigorate both engines of the economy. The best way to achieve this is to maintain macroeconomic stability, to develop a business environment that promotes investment and job creation, and to increase the stock of physical and human capital. • Foreign Direct Investment is key to Kenya’s development agenda. Since domestic savings are low, attracting FDI would supplement domestic savings in financing Kenya’s growth agenda. Kenya should aggressively seek more productivity enhancing FDI to diversify its economy, and develop its private sector, encouraging technology transfer to sharpen its competitive edge in the external market. • The ultimate objective of Kenya’s development strategy is to make it more inclusive. The new administration promises to make growth more inclusive. This can only be done through reforms to promote economic diversification and job creation, and tackling infrastructure gaps. Key Recommendations to make poverty history • A system of poverty monitoring is needed—with nationally representative household budget surveys as a foundation—to understand how, where and why poverty is changing, and to inform public policy. Without more frequent surveys, there has been a missed opportunity to understand whether economic gains and government policy have generated pathways out of poverty for the poor. • Sustained poverty reduction requires the creation of more productive jobs. To encourage the growth of the low and middle skills jobs that will represent pathways out of poverty for the poor, the government can work to improve the competitiveness of manufactured exports, and the investment and business environment. In addition, as the majority of Kenya’s poor depend on agriculture for their livelihood, helping them gain access to inputs and markets can drive poverty reduction in the short term. • Poverty reduction can be accelerated with greater equity in Kenyan society through more effective public spending and stronger cash transfer programs. Public spending should work to remove the role that geography, gender, ethnicity and wealth play in influencing access to key services, so that everyone is in a good position to seize the opportunities being generated in a growing economy. Strengthening, harmonizing and expanding Kenya’s array of cash transfer programs with will help reduce poverty, by enabling poor households to consume more, invest in productive assets, and achieve their education and health goals. EXECUTIVE SUMMARY Kenya Rising? of exogenous shocks that have periodically set T he new Kenyan government has taken office at back the economy. There have been droughts, oil a time when there is a new optimism for Kenya price spikes and the blow back from the recession rising. While Kenya’s economic performance over in the European Union, a major trading partner. the past decade has lagged behind the average Kenya’s neighboring countries have experienced for sub-Saharan Africa, even when resource rich most of the same shocks, yet managed more countries are excluded, there is palpable sense robust growth. Why has Kenya lagged? This that Kenya has turned a corner with peaceful report delves into this problem and identifies elections in March 2013, and the smooth transition the structural issues that separate Kenya from its of power in April. If these developments reflect peers. Fortunately, many of the challenges, such the maturing of Kenya’s political system, there as the business environment constraints affecting is equal optimism that Kenya has put behind the the private sector, can be addressed in the short- troubling economic periods that have regularly and medium-term. followed its previous election cycles. Kenya’s Figure 1: Kenya is lagging its peers economic performance for 2012, proved stronger GDP growth 2003-2012 than anticipated at 4.6 percent, historically high 8 compared with recent election year periods, and 7 the forecasts for 2013 (5.7 percent) and 2014 6 5.8 Percent Growth (6.0 percent) are encouraging. Yet Kenya could do 5 4.6 much better, and there is no doubt that the new 4 government wants to unleash the potential of the 3 Kenyan economy. 2 1 What will it take for the Kenyan economy to 0 break its pattern of underperformance? Over the 1998/2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 past decade, Kenya’s economy grew at an average Kenya Sub-Saharan Africa excl. ZAF Source: World Bank estimates of 3.8 percent. This is better than in previous decades, but below its potential, its ambition, and its peers. While an increasing number of African For Kenya to grow higher, it needs more stability countries have already reached Middle Income and a new approach to economic development. status, Kenya has lagged behind. Today, out of 48 For Kenya to grow beyond 5 percent, it needs to sub-Saharan African countries, 22 countries have enhance the contribution of exports as an engine of reached a per-capita income of US$ 1025—the growth which is now dominated by consumption. official threshold of middle income. At about US$ Today, net exports are a drag on growth, having 820 Kenya’s GDP per capita, it ranks 24th and only reduced overall growth by 4.1 percent in 2012 represents about half the sub-Saharan Africa (SSA) (see Figure 1.6)—and as reflected in a large and average. Excluding South Africa, sub-Saharan widening current account deficit. This is holding Africa grew at an average of 6 percent since back the growth momentum. If Kenya was to 2002. East Africa as a whole grew even more, at balance its external position, i.e. matching imports 6.5 percent, and without Kenya it would have with exports, while maintaining current levels of grown at almost 7 percent (see Figure 1). Kenya consumption and investments, it’s overall growth has been following, not leading Africa’s growth would already be at 8 percent. momentum. Part of the problem has been a series 1 Devarajan Shantayanan/Wolfgang Fengler. 2012. Is Africa’s Recent growth sustainable?, IFRI, Paris. June 2013 | Edition No. 8 v Executive Summary Kenya entered 2013 on a strong economic footing, the stock of physical capital, improvements in and peaceful elections are giving it an additional human capital (in particular through education boost. Agriculture and manufacturing are and health) are needed to raise potential output. benefitting from stable rains, which both stimulate production and drive down the costs of Kenya’s The state of poverty T hydro-generated electricity. Monetary and fiscal oday, Kenya’s poverty rate is estimated to be policies also contributed to the recovery. Declining in the range of between 34 and 42 percent. inflation allowed the Central Bank of Kenya to Given the absence of a household survey since lower its benchmark interest rate to 8.5 percent, 2005—the year Kenya last conducted one, more and the shift from recurrent towards development recent poverty estimates are based on projections, expenditure, especially for infrastructure, is also and depend on various assumptions, including supporting the growth momentum. on the evolution of inequality in Kenya. If recent economic growth benefited all Kenyans in a However, the current growth model cannot spur similar way (i.e. if inequality remained stable), the growth rates of 10 percent, as poverty rate would be close to 38 envisaged under Vision 2030. percent. But poverty would be lower The structure of the Kenyan (or higher) if inequality increased economy would need to change (or decreased) say by one percent Kenya’s poverty rate in order to attain sustainable per year, poverty would have fallen growth rates of around 10 is estimated to be in percent. First, the overall level of the range of between to only 42 percent (34 percent). A more complex projection using the savings and investment, needs 34 and 42 percent. more recent census and other data to increase in order to raise the Given the absence of a sources of social wellbeing come economy’s potential growth. To household survey to a similar conclusion: Over the raise the investment rate, the since 2005 last decade, Kenya’s poverty has economy would also need to probably declined slowly (at about 1 percentage attract more foreign savings in form of foreign point per year), but remains at very high absolute direct investment. Second, exports, which have levels about 42 percent in 2009. been subdued for almost a decade, would have to contribute more to growth. As in other countries, poverty in Kenya is much deeper and much more pronounced in rural Several challenges would need to be addressed and remote regions. Poverty rates are highest for the economy to make this desired shift. in the arid and semi-arid regions in the north The quantity and quality of public infrastructure and north east. Geographically, areas with very need further improvement, in order to lower little annual rainfall, and thus, low agricultural transport costs and facilitate trade in goods and potential have acute poverty. These regions have services (tourism). Facilitating trade also requires also been historically neglected, reflecting Kenya’s streamlining procedures for exporting and unbalanced geographical development. In 2005, importing, as well as efforts to avoid overvaluation poverty rates in arid regions (78 percent) were of the real exchange rate. The exchange rate should nearly double the poverty rates in medium and support exports and make domestic goods more high potential agricultural areas (with a poverty competitive vis-à-vis imports. A weak business rate of 41 percent). environment is another constraint to investment and economic activity: a more conducive business However, Kenya’s poorest places are not the environment would generate new economic same places where most of the poor live (see activity, which would translate into more jobs, Figure 2). Kenya’s lagging areas are sparsely including the formal sector. In addition to raising populated and more isolated from it’s urban vi June 2013 | Edition No. 8 Executive Summary Figure 2: Has growth and inequality driven poverty down? Between 1989 and 2009, Kenya has also experienced positive developments in several 60 non-income dimensions of poverty, but not all 49 of them. On average, Kenyans are increasingly Poverty Headcount (percent) 49 50 47 healthy and more educated, enjoying better living 42 40 38 conditions, and an expanded set of consumption 34 opportunities. At the same time, a large fraction of 30 Inequality Scenarios Long term change in gini: the population continues to live with sub-standard 20 +1% 0% access to water, sanitation and energy (see Table -1% 2005 KIHBS poverty rate 1). Inequality of opportunity is quite high. Indeed, 10 for many, the sheer luck of where in the country 1990 1993 1996 1999 2002 2005 2008 2011 a person is born, one‘s ethnicity and one’s family Source: World Bank wealth play an outsize role in determining access to basic opportunities. economic engines—Nairobi, Mombasa and Kisumu. The majority of Kenya’s poor live in the When it comes to delivery of critical opportunities denser and higher potential agricultural zones, in and services, Kenya’s performance is mixed: the vicinity of large urban centers. In this context, Extraordinary success stands side by side with better integration of high potential rural areas resounding failures (see Table 1): to large urban markets and providing access to quality basic services everywhere is critical. - In the past 8 years, Kenya has experienced Kenya’s development—as elsewhere—has been a true telecommunications revolution with unbalanced geographically, and characterized by household phone ownership increasing at an the growth of economic and population density average rate of 30 percent per year since 2005. in towns and cities. Urbanization is driven by an - Kenya has also made significant gains in making increasingly mobile and educated population, that basic education accessible broadly—primary is meeting economic opportunities where they enrollment rates are now almost universal. are created. Facilitating migration The next challenge for Kenya is to and managing the urbanization strengthen classroom learning, process are two strategies that will by reducing teacher absenteeism support poverty reduction in Kenya. Kenyans are increasingly (a recent study found that almost At the same time, promoting a more one in two teachers (45 percent) healthy and more productive and healthy agricultural are not in the classroom during educated, enjoying sector supports this process, helping better living conditions scheduled teaching times) and towns and cities to prosper, as well as improving teacher knowledge the villages that many people leave behind. and performance through training. - This past decade has been a major success for As expected, poverty is strongly associated with child health: under-five mortality fell by over 4 low levels of education and large households. percent per year, driven in part by the scaling Primary and secondary school completion rates of evidence-based child health interventions. are the lowest amongst the poorest individuals. Maternal health and child nutrition however, In 2009, the average size of households among remains a challenge. the poorest 20 percent of households was 5.2 - While household access to electricity increased compared to a national average of 4.3 and an in Kenya between 1989 and 2009 by about 4 average of 3.5 among the wealthiest households. percent per year, over three in four households 2 This projection relies on estimating the relationship between household characteristics and consumption using regression analysis from a survey with consumption data and applying these estimates to censuses or surveys that do not measure consumption directly. The strongest assumption with this approach is that the parameter estimates that capture the relationship between household assets and consumption are stable, i.e. they do not change over time. June 2013 | Edition No. 8 vii viii Figure 3: Focusing on poor regions …or poor people? June 2013 | Edition No. 8 Source: World Bank Executive Summary Executive Summary still do not have access to electricity, and the In shifting gears, Kenya also needs to complement improvements were more concentrated in the rapid growth with equity. At times Kenya achieved counties around Nairobi. higher growth but these periods were short- - Access to clean water and decent sanitation lived because—like a car driving at 80 kilometers facilities remains elusive to a majority of per hour in the third gear—Kenya has not been Kenyans. There is evidence that access to able to shift gears to grow at a higher speed for clean water is not keeping up with rates of a sustained period. But in addition to addressing urbanization in the towns and cities outside of barriers to higher sustained growth, Kenya needs Nairobi, where access to piped water or public to address its still quite high levels of poverty. This tap water declined by over 2 percent per year in turn, will most probably require reducing its between 1989 and 2009. high levels of inequality. Indeed, Kenya can only eliminate extreme poverty by 2030, the World Making poverty history Bank’s global poverty target, if it reduces poverty K enya needs higher growth to reduce poverty faster. With the historic GDP growth rates of 4-5 percent, average per-capita incomes are only by 2 percentage points each year. Such a high rate of poverty reduction is only possible if growth is accompanied by a reduction in inequality. rising by about two percent a year, given that This means that the poor need to benefit to a Kenya’s population growth rate is still at a high disproportionate extent from economic growth, 2.6 percent. If the wealthier are benefitting more, both through new economic opportunities and by which is probable even though not proven, the ensuring that safety nets are adequately buffering poverty reduction benefits of Kenya’s moderate the vulnerable form shocks. growth momentum have arguably been very limited. Table 1: Kenya: A country of contrasts in service delivery Social and infrastructure Assessment Trend Indicators indicators Connectivity 100 percent connectivity Kenya experienced a true Percentage of households almost achieved; calling rates telecoms revolution with at least one mobile among the lowest in the phone increased 30 percent world per year since 2005 Education Primary enrollment almost Some improvement but low universal but quality remains “value for money� a major challenge Health Sharp reduction in child Some improvements Under-five mortality fell by mortality; high levels of over 4 percent per year since maternal mortality 2000 Electricity While household access to Some improvement Households with access to electricity increased in Kenya electricity increased by over between 1999 and 1989 the 4 percent per year since 1990 improvements were more concentrated in the counties around Nairobi. Water (urban) Connectivity did not keep the Deteriorating Percent of households with same pace as urban popula- access to piped water or tion growth public tap water declined by over 2 percent per year since 1990 in urban areas outside Nairobi Source: World Bank computations - Note: Colors indicate strong progress (green), some progress (orange), no progress (red). June 2013 | Edition No. 8 ix Executive Summary The analyses presented in this report point to playing field in access to key opportunities— five elements of a poverty reduction strategy. such as quality education, energy, water and These are: sanitation—will be key to increasing equity in the Kenyan society. (i) Fostering pro-poor economic growth and job creation. To encourage the growth of (iv) Using public spending to make key low and middle skills jobs, especially in opportunities available to Kenyans of all manufacturing, the government needs backgrounds. Particularly important in to improve export competitiveness and this regard is ensuring that children from improve the investment and business households in all income groups have environment more broadly. access to quality education, which will have (ii) Enhancing the productivity of smallholder positive effects on poverty reduction, both farms. Since the majority of Kenya’s poor through a growth effect (skilled workers depend on smallholder agriculture for their earn more) and an inequality effect (having livelihood, increasing their productivity a higher supply of skills would drive down through the use of fertilizer, improved seeds the skills premium and reduce inequality). and access to markets, will lead to significant poverty reduction in the short to medium (v) Investing in a system of routine household term. budget surveys to monitor poverty and inequality. To convincingly monitor the (iii) Strengthening and expanding the cash impact of Government policies on household transfer programs that protect and provide consumption, equity and poverty reduction, income support to the poor. Stronger cash comprehensive and comparable household transfer programs and more equitable and surveys need to be implemented regularly. effective public spending for leveling the x June 2013 | Edition No. 8 Executive Summary The State of Kenya’s Economy June 2013 | Edition No. 8 xi The State of Kenya’s Economy 1. Economic Performance T he Kenyan economy has stabilized and could again be in a position for a takeoff. Inflation has declined to below the 5 percent target, and expectations are anchored at a lower level for the rest of 2013, the international reserves have climbed to over US$ 5b (over four months of import cover), public debt to GDP level has declined to below 45 percent, and credit has started to flow back to finance economic activities. The optimism of Kenya’s economy is reflected by high volumes of trading in the fixed income securities and equities market. GDP growth in 2012 was 4.6 percent, and is projected to grow to 5.7 and 6 percent in 2013 and 2014, respectively. Despite the optimism, risks do remain. The economy is still vulnerable to exogenous shocks as the large current account deficit threatens macroeconomic stability, the real appreciation of the shilling is eroding Kenya’s competitiveness and stifling the export sector, which is supposed to be at the center point for poverty reduction. 1.1 A resilient economy in times of adversity 1.1: Growth picked up during the second half of 2012 K enya grew at 4.6 percent in 2012 amid a weak global economy. In early 2012, the economy was weak, mainly due to high interest 9 8 7.2 8.3 Quarterly GDP 7 rates resulting from high inflation which peaked 6 6.1 5.3 5.2 at the end of 2011. Over the course of 2012, 4.8 Percent 5 4.4 4.5 4.0 4.0 the Government succeeded in stabilizing the 4 3.5 3 economy. Inflation declined to below 5 percent 2 1.4 at the end of 2012—overall average for 2012 1 was 9.6 percent—which helped to stabilize the 0 1 2 3 4 1 2 3 4 1 2 3 4 exchange rate, and allowed for a gradual easing of monetary policy. However, that success came 2010 2011 2012 at a cost. The tight monetary policy stance which Source: World Bank Staff calculations based on KNBS data started in the second half of 2011 triggered a noticeable slowdown in economic activity in 2012, African Community (EAC) countries. For the last as domestic demand remained low on account of ten years, other than in 2005, Kenya recorded the high cost of capital. lower annual GDP growth than the average for sub-Saharan Africa, and compared to its neighbors Economic growth picked up in the second half in the East African Community. Kenya’s annual of 2012. After growing at only 4.2 percent in the growth rate for the decade averaged 4.6 percent, first half of 2012, the economy accelerated to 4.9 compared to 6 percent for SSA, 6.9 percent for percent in the second half. Fourth quarter growth Tanzania, 7.1 percent for Uganda, and 7.2 percent was 5.3 percent, which represented the highest for Rwanda. Since the beginning of the global crisis economic performance since the end of 2010 in 2008, the economy has struggled to recover when the economy grew at 8.3 percent. The strong from a number of shocks, including the aftermath performance in the second half of the year was of the violence that followed the elections in driven by domestic demand, as exports continued 2007, reduced demand in the Euro zone, Kenya’s to suffer from the weak global environment. largest trading partner, and the impact resulting from high international oil prices. These shocks Kenya’s economic performance continued to resulted in low export growth and higher imports. lag behind the rest of sub-Saharan Africa (SSA), Unfavorable climatic conditions hurt agriculture particularly, when compared to other East output, and hydro power generation in 2009 2 June 2013 | Edition No. 8 The State of Kenya’s Economy Figure 1.2: Kenya is lagging behind SSA 8 Average Growth Rate in EAC 2003-2012 8 7 7.1 7.2 6.9 6 7 5.8 6.0 Perecnt Growth 6 Percent Growth 5 4.6 4 5 4.6 3 4 3.5 2 3 1 2 0 1 0 Burundi Kenya SSA excl ZAF Tanzania Uganda Rwanda Kenya Sub-Saharan Africa excl. ZAF Source: World Bank Staff calculations based on Global Economic Prospects, 2013 and 2010. Security threats impacted growth in good rainfall in the first quarter, which boosted the service sector, through reduced numbers of the production of maize, beans and sugarcane tourists. among other crops. Tea production declined by 2.2 percent in 2012, due to adverse weather Economic growth in 2012 was broad based. Unlike conditions characterized by frost attack in some in 2011 when growth was driven predominantly tea growing areas. A bumper harvest of staple by service sectors, industry and agriculture also food—maize and beans—eased their prices had a good year in 2012. Agriculture output grew and contributed to reduced food inflation in the by 3.8 percent, more than twice its growth in second half of 2012. Maize production increased 2011. Agriculture’s strong growth resulted from by 16.3 percent, as Kenya produced 40 million Box 1.1: Why is Kenya underperforming? Kenya’s economic growth rate has not matched even once Africa’s growth rate in the course of the past decade, though the two were close in most years. Kenya’s relatively weak economic performance can be attributed to three main factors: internal shocks, lack of natural resources, and economic fundamentals. Internal shocks explain the widening gap between Kenya’s and Africa’s growth rate in 2008-09 and 2011, while the other two explain Kenya’s overall underperformance. Kenya’s economy was hit by several disruptive events over the last five years. The post-election violence of 2008 caused economic activity to plummet in fear of violence and political uncertainty. As the political situation calmed, the agriculture sector faced a severe drought in 2009 which continued to dampen economic output. The outlook improved in 2010, but 2011 brought signs of macroeconomic instability, fueled by expansionary monetary policy; and economic growth again slowed. One key reason behind Africa’s strong performance over the past decade has been the commodity boom. Various natural resources were discovered across the continent, which, in a global environment of raising demand and prices, generated a substantial share of the economic growth. Kenya has not been as fortunate –though this may change in the medium-term-, hence this explains part of the gap vis-à-vis the continent’s performance. The remainder of the gap in growth, in particular compared to other East African countries, can be explained by differences in economic fundamentals. First, Kenyans save less than their neighbors, hence they invest less, and investment is a key ingredient for rapid and sustainable growth. Second, infrastructure bottlenecks continue to be a drag on economic activity. The dire situation in railways, the inefficiencies at the Mombasa port and the congestion on Kenya’s roads attest to this. Finally, the business environment has not seen the improvement it needs to unleash Kenya’s growth potential. Other neighbors, e.g. Rwanda, have made great progress in streamlining business regulations. In Kenya, on the other hand, paying taxes, getting electricity, registering a property, and starting a business, continue to be excessively time-consuming and costly. Source: World Bank June 2013 | Edition No. 8 3 The State of Kenya’s Economy bags in 2012 compared to 34.4 million bags in Figure 1.4: Hydro generation rebounded in 2012 2011. Wheat production increased by 54 percent 400 in 2012 to 163 thousand tonnes. 350 300 Industrial output rose by 4.5 percent in 2012 250 Million KWh compared to 2.9 percent in the same period for 200 2011. The main driver of industry growth was 150 100 electricity and the water sector which grew by 50 10.3 percent in 2012, after a negative growth in 0 2011. Installed electricity capacity expanded by Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar 4.7 percent from 1534 MW in 2011 to 1606 MW 2010 2011 2012 2013 in 2012. Hydropower generation grew by 21.6 Hydro Geo Thermal Thermal percent in 2012 accounted for 50.7 percent of Source: World Bank staff calculations based on KNBS data total power generations. Manufacturing sector recorded a 3.1 percent growth in 2012, compared election jitters. Building and Construction is the to 3.4 percent in the same period in 2011. The only subsector which recorded a higher growth slowdown in manufacturing was as a result of rate in 2012 when compared to 2011. It grew by 4.8 stiff competition from imported goods, high costs percent in 2012, compared to 4.3 percent in 2011. of credit and political uncertainties, due to pre- The growth in this subsector was driven by loans and advances which increased by 36.2 percent Figure 1.3: Growth in 2012 was broad based in 2012, and increased government expenditure 7 in the Ministry of Roads which increased by 29 6.3 6 percent in 2012. Yearly GDP Growth (percent) 5.4 5.2 5 4.6 4.5 Although most service sectors grew in 2012, 4 3.8 3.8 growth was in many instances lower compared 3 2.9 to 2011. From 5.2 percent growth in 2011, services 2 1.5 grew by 4.6 percent in 2012. The deceleration of 1 growth came as a result of tight monetary policy. All other subsectors recorded a slowdown in growth 0 2010 2011 2012 as: wholesale trade grew at 6.4 in 2012, compared Agriculture Industry Services with 7.3 percent in 2011; financial intermediation Source: World Bank Staff calculations based on KNBS data growth stood at 6.5 percent in 2012 against 7.8 Figure 1.5: Growth in services declined in all sectors including tourism except public administration 160 Number of tourist arrival in thousands Other services 140 Education 120 Public administration Real estate, renting, 100 business services 80 Financial intermediation 60 Transport and communication 40 Hotels and restaurants 20 Wholesale and retail trade 0 0 1 2 3 4 5 6 7 8 9 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Growth Rates (percent) 2012 2011 2011 2012 Source: World Bank staff calculations based on KNBS data 4 June 2013 | Edition No. 8 The State of Kenya’s Economy percent in 2011; real estate growth slowed to 3.3 Investment growth was strong in 2012, but percent from 3.6 percent in 2011; and, hotels and lower than in 2011. Gross investment moderated tourism activity grew by 2.6 percent (compared to slightly in 2012, as high interest rates and political 5.0 percent in 2011). Tourist arrivals at both Jomo uncertainty slowed down demand for investment Kenyatta and Moi International Airports declined goods. Gross fixed capital formation grew by 11.5 by 6.1 percent in 2012—from 1.8 million visitors in percent in 2012, down from 12.6 percent in 2011. 2011 to 1.7 million in 2012—due to security threats The drop is explained by other machinery and in the region and the Euro-zone economic crisis. equipment (which constitutes 38 percent of gross On the other hand, transport and communication fixed capital formation) which grew by 11.4 percent growth accelerated from 4.1 percent in 2011 to in 2012, significantly down from 27.1 percent in 5.3 percent in 2012. 2011, as a result of political uncertainties related to 2013 election jitters. Building structures (which Private consumption continues to underpin constitute 43 percent) grew modestly in 2012 at 4.4 aggregate demand and growth. Real domestic percent, up from 3.5 percent in 2011, due to the demand grew sharply in 2012, supported by a prevailing high interest rate. Transport equipment recovery in private and government consumption. (constituting 18 percent) grew sharply in 2012, Gross domestic expenditure grew by 6.8 percent growing at 27 percent as demand for vehicles to in 2012, up from 5.8 percent in 2011. This was carry out elections campaigns intensified. mainly driven by private consumption (which constitute 65 percent of aggregate demand and 79 Net exports continued to be a drag on Kenya’s percent of Real GDP) growth which increased to GDP growth, with a negative contribution of 4.1 5.5 percent in 2012 from 3.0 percent in 2011. The percentage points. Even though both exports mild growth in private consumption was explained and imports growth moderated in 2012, the gap by a high interest regime in 2012. Government between exports and imports widened. In local final consumption expenditure (which constitutes currency terms, exports growth slowed down to 12 percent of aggregate demand and 15 percent 4.7 percent in 2012, compared to 6.6 percent in of Real GDP) grew sharply by 9.3 percent in 2011, while growth of imports declined from 15.6 2012, the highest in 5 years, to cater for the percent to 12.5 percent in the same period. The new constitutional offices and election related strength in imports growth reflect the importation expenditure. of transport equipment and machinery for oil and gas exploration, while the weakness in export Figure 1.6: Consumption continues to be the key driver of Kenya’s growth offsetting net export weakness growth is mainly due to a strong shilling and weak GDP growth through expenditure approach global demand, especially in the euro zone. 8 6.2 6.6 6 5.7 High frequency indicators present a picture that 4.5 4 3.5 4.1 is broadly consistent with subdued growth. 3.1 2.6 High frequency data reflected the weakness of Percent 2.1 2 1.3 1.2 0.3 the demand side, and underperformance on 0 2007 the supply side. On the production side, cement -0.7 2008 2009 2010 2011 2012 -2 -2.1 production plummeted from a growth of 20.7 -2.7 -4 -2.8 -3.5 percent in 2011, to 3.6 percent in 2012, while -4.1 sales dropped from a growth of 24.7 percent to 1.7 -6 Consumption Investment Net exports GDP percent in the same period. The fall was attributed Source: World Bank staff calculation Based on KNBS data to low access to credit and higher interest rates. Note: Statistical discrepancy explains the difference the sum of Total motor vehicle registrations declined by 15.9 (consumption, investment and net exports) and actual GDP growth June 2013 | Edition No. 8 5 The State of Kenya’s Economy Figure 1.7: inflationary pressures have come under control 30 Contributi on to Overall Inflati on 100 25 90 38.2 80 20 70 60 Percent Percent 15 23.4 50 40 10 30 20 38.4 5 10 0 0 10 Au 0 10 10 11 Au 1 11 11 12 Au 2 12 M 0 Au 0 No 0 Fe 0 M 1 Au 1 12 No 1 Fe 1 13 3 M 2 Au 2 No 2 Fe 2 13 3 -1 -1 -1 -1 1 -1 1 1 1 -1 1 1 1 -1 1 1 -1 b- g- v- b- g- v- b- g- v- b- b- g- v- g- v- b- b- g- v- b- ay ay ay ay ay ay ay ay No No No Fe Fe Fe Fe Fe M M M M M Food inflation Transport Inflation Core Inflation Overall Inflation Food Energy Core Source: World Bank staff calculations based on KNBS data in 2012, after a 5 percent growth in 2011, and discipline. Despite a decline in total revenue of motorcycle sales declined by 33 percent in 2012, 0.8 percent of GDP in 2011/12, the primary deficit compared to a 20 percent growth in 2011. was kept at around 2 percent of GDP, and the same deficit is projected to be achieved in 2013. Inflationary pressure moderated in 2012. Month on month overall inflation declined for 12 The aggregate fiscal position remains sound, consecutive months in 2012 from 18.9 percent despite the overall fiscal deficit having increased in December 2011 to 3.2 percent in December in 2011/12 and 2012/13. The deterioration of the 2012. Average annual inflation declined from 14 deficit (commitment basis3) from 4.5 percent of percent in 2011, to 9.6 percent in 2012. This was GDP in 2010/11, to 5.6 percent in 2011/12, and as a result of monetary policy tightening, and the the budgeted 6.7 percent in 2012/13 has come as absence of any new fuel and food price shocks. On a result of increased spending on infrastructure, the contrary, international oil prices fell in 2012, in particular geothermal power and roads. The and abundant rainfall reduced electricity prices. boost in development expenditure has been Bumper harvests in quarter three eased food accompanied by a constraint on recurrent inflation. Core inflation (which excludes food and spending: capital spending increased by 1.3 oil price movements) declined from 11.6 percent percent of GDP in 2011/12, while recurrent in December 2011, to 5.5 percent in December spending declined by 1.5 percent of GDP. 2012. Overall, Inflation has edged a notch higher Figure 1.8: Fiscal position remains strong despite in the first quarter of 2013, but remains below the deterioration of the overall balance medium target level of 5 percent. 35 30 29.5 29.1 28.9 24.9 24.0 1.2 Kenya’s fiscal prudency is paying off 25 21.9 23.9 22.8 T Percent of GDP 20 he government’s fiscal framework continues 15 to support macroeconomic stability. The 10 government has successfully maintained fiscal 5 1.5 discipline in the face of election year pressures, 0 and the high costs of security operations in -5 -2.0 -4.4 -1.8 -4.5 -1.8 -2.8 -5.6 Somalia. Moreover, the economic slowdown—and -10 1998/2009 2009/2010 2010/2011 2011/2012 the resulting lower than anticipated revenue—did Government Revenue Government Expenditure not shake the Government’s commitment to fiscal Overall Balance including grants Primary Balance Source: World Bank Staff calculations based on Ministry of Finance data 3 The gap between the projected and actual deficits is a result of expected underperformance in spending. 6 June 2013 | Edition No. 8 The State of Kenya’s Economy Kenya is on track to return public debt levels to the maturity structure of Kenya’s external debt is their healthy 2007 level of less than 40 percent. long term, with over 77 percent with a maturity This level of public debt would give it ample room of over 10 years, 20.7 percent with 5-10 years for policy maneuvering. Lowering public debt maturity, and less than 3 percent with less than would also help mitigate the widening current 4 year maturity.4 The external debt is mainly account deficit. denominated in the Euro (33 percent), the US Dollar (31 percent) and the Japanese Yen (16 Figure 1.9: Kenya’s public debt declined in 2012 percent).5 Government’s net domestic debt fell 60 from 21 percent of GDP at the end of 2011, to 50 20.4 percent of GDP at the end of 2012. Most of the domestic debt is held by commercial banks in 40 21.8 22.2 20.0 form of T-bills and government bonds (comprising Percent of GDP 30 18.9 21.0 of 19 percent and 75 percent of domestic debt, 18.8 19.8 22.7 20.4 18.1 17.4 respectively). The share of domestic debt held 20 by non-banks increased from 40.8 percent to 34.9 35.0 33.9 10 27.7 24.0 21.0 23.3 22.9 23.1 26.2 22.5 43 percent of the total between 2011 and 2012, reflecting a diversification of the domestic investor 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 base.6 External debt Domestic debt Figure 1.10: Yield curve movements in the last 9 months Source: IMF/World Bank: Kenya DSA 2013 reflected political developments 14 Government of Kenya Securities Yield Curve Fiscal policy has strengthened Kenya’s debt 13 sustainability, as the trend in rising public debt 12 was reversed in 2012. Kenya’s total net public debt-to-GDP ratio declined in 2012, as a result of 11 Percent prudent fiscal policy and a stable macroeconomic 10 environment. At the end of 2012, Kenya’s public 9 debt stood at 42.9 percent of GDP, compared to 8 47.2 percent in 2011. Public external debt declined 7 from 26.2 percent to 22.5 percent of GDP in the 3M 6M 1Y 2Y 5Y 10Y 15Y 20Y 25Y 30Y Tenor same period, mainly on account of exchange rate 30-Sep-12 30-Dec-12 movement, and in part on the retirement of the Source: World Bank Staff calculations based on Bloomberg data syndicated loans issued in 2011. The yield curves movements reflected political The structure of public debt is favorable. Most developments in the last 9 months. The yield of Kenya’s public external debt remains on curve shifted outwards in response to political concessional terms, although its commercial uncertainty of the March 2013 general election. component increased to about 10 percent at the Between September 2012 and March 2013, the end of 2012, mainly as a result of a syndicated average interest rates on government securities loan of about US$ 600 million. External debt increased by an average of 170 basis points (for portfolio is mainly owed to multilateral creditors securities of over 10 years), 307 basis points (over (59.9 percent), followed by bilateral (31.2 percent) one year but up to 10 years), and 188 percent and commercial (7.1 percent) creditors. Overall, (less than one year). However by May 2013, the 4 Data for June 2011, Source: Ministry of Finance: Annual Public Debt Management Report December 2011. 5 Among Others: Yuan - 6 percent and Sterling pounds - 5 percent. 6 By March 2013, holders of domestic debt were distributed as follows Commercial Banks 48.45 percent, non-banks 45.1 percent, CBK 5.6 percent and non-residents 0.9 percent. Non-banks include Pension Funds (majority in terms of share), insurance companies, and parastatals. June 2013 | Edition No. 8 7 The State of Kenya’s Economy risk related to elections has started to decline, and Revenues declined from a peak of 23.9 percent of the yield curve has shifted downwards (see figure GDP in 2009/10, to 22.8 percent in 2011/12. In the 1.10). Between March and May 2013, yields have first half of 2012/2013 tax revenues continued to reduced by 24 basis points (securities of over 10 decline further to 10.2 percent of GDP, against the years), 95 basis points (securities of over one year target of 12.4 percent, and less than the 2010/11 but up to 10 years), and 115 basis points (securities outturn of 10.5 percent of GDP, mainly on account of one years and less). of reduced VAT collection. The decline in public debt has increased the fiscal Lower revenue collection is a consequence of space which Kenya plans to use to borrow on weaker VAT and excise duty collection. VAT and international markets. A US$ 1 billion sovereign excise revenues declined by 0.6 percent and bond issue is planned for September 2013. The 0.5 percent of GDP, respectively in 2011/12. government will use the funds partly to repay Parliament’s delay in approving the new VAT Bill the syndicated loans issued in 2011/12, and on which targets were based, partly explains the partly to finance infrastructure projects. Debt lower VAT collection. Government’s collection of sustainability indicators are not expected to income taxes, which accounts for about 40 percent deteriorate significantly following the sovereign of domestic tax revenue, increased slightly. bond issuance. Figure 1.11: Principle sources of government revenue have remained the same over the years The government faced some challenges in 11 implementing fiscal policy in 2012/13, as revenue 9.3 9.5 8.8 underperformed, while pressures for increasing 9 personnel expenditures through higher wages and Percent of GDP 7 new constitutional offices grew. For the first half 6.0 6.2 5.6 of 2012/13, the overall fiscal deficit deteriorated 5 to 3.2 percent as a share of GDP, slightly lower than the targeted level of 3.3 percent of GDP, 3 3.0 2.9 2.4 but higher than the 2 percent of GDP in the same 1.7 1.7 1.6 period in 2011/2012. The deterioration came as a 1 2009/2010 2010/2011 2011/2012 result of lower revenue and increased government Income Tax VAT Import Duty Excise Duty spending in the first half of 2012/2013. The deficit Source: World Bank calculations based on data from Ministry of Finance was financed largely by domestic borrowing (of 3 percent of GDP), while new foreign borrowing Despite lower collection, fiscal revenue was was 0.3 percent of GDP. The primary fiscal deficit sufficient to fully finance the budget’s recurrent reached 1.6 percent of GDP in the first half of the expenditure, and part of its capital spending. The fiscal year, higher than the 0.75 percent of GDP ratio of total revenue to recurrent expenditure has deficit in the same period of 2011/12. increased from 1.07 in 2007/08 to 1.16 in 2011/12. This signals that fiscal revenue is catering for the Government revenue as share of GDP has entire recurrent spending, and part of capital declined slightly over the last two years. spending. Table 1.1: Kenya’s revenue mobilization fully catering for its recurrent expenditure and part of development 2007/08 2008/09 2009/10 2010/11 2011/12 Total Revenue (KES billion 432.2 487.9 586.4 667.5 748.2 Recurrent spending (KES billion) 403.4 435.5 510.5 592.4 647.1 Revenue to Recurrent spending ratio 1.07 1.12 1.15 1.13 1.16 Source: World Bank calculations based on data from Ministry of Finance 8 June 2013 | Edition No. 8 The State of Kenya’s Economy The government is constraining the growth of The increased development expenditure its overall spending, but allowing development was directed to infrastructure. Ministry of spending to increase. As revenue underperformed, Roads accounted for 25.9 percent of the actual government has rationalized expenditure by ministerial development expenditure. The other cutting recurrent spending, but allowing capital top beneficiaries of ministerial development spending to increase. In 2011/12, government expenditure were the Ministry of State for spending declined from 29.1 percent of GDP to Planning and National Development and Vision 28.9 percent. However, spending cuts were fully 2030, which spent 16.27 percent (for CDF absorbed by reductions in recurrent budgets (from projects), the Ministry of Finance (7.64 percent for 21.2 percent of GDP in 2010/11 to 19.7 percent in LATF-urban investment), Ministry of Energy (7.10 2011/12), while capital spending increased (from percent for geothermal power generation), and 7.9 to 9.2 percent of GDP). The fiscal outturn in the Ministry of Water and Irrigation (4.06 percent the first half of fiscal year 2012/13 showed an for infrastructure). Infrastructure improvements overall increase in government expenditure to are among the key factors that are expected to 13.4 percent of GDP, compared to 12.5 percent of boost Kenya’s economic growth. GDP for the same period in 2011/12. Human capital also matters for economic Figure 1.12: Budget Cuts absorbed by recurrent spending development and some restructuring in spending while development spending was increased 35 on social sectors may be needed. The quantity and quality of human capital is determined by health 30 and education outcomes. In this regard, health 25 and education spending accounts for 2.2 percent 20 and 6.4 percent of GDP, respectively. The spending 20.77 21.25 19.72 on education is comparable or higher than in 15 other peer countries, which is good. However, 10 education outcomes depend also on the quality of 5 8.73 7.87 9.16 service provision, and here there is lot of room for 0 improvement, in the efficiency and effectiveness 2009/2010 2010/2011 Development Expenditure 2011/2012 Recurrent Expenditure of the education system. Health expenditure on the other hand would need to be increased—so Source: World Bank staff calculations based on Ministry of Finance data will the efficiency of spending—to be able to meet Kenya’s health MDGs. Figure 1.13: Implementation of the budget remains a major challenge Environment Protection, Budget Implementati on Rates Dec 2012 Water and Housing Environment Protection, Water Social Protection, Culture and Recreation and housing 39 National Security Energy, Infrastructure and ICT 45 Public Administration and International Social Protection, Culture and Recreation 59 Governance, Justice, Law and Order Agriculture and Rural Development 61 Education Health 71 Health Public Administration and International 74 General Economic, Commercial and General Economic, Commercial and Labour 75 Labour Affairs Governance, Justice, Law Energy, Infrastructure and ICT 83 and Order Education 93 Agriculture and Rural Development National Security 100 0 40 80 120 KES Billion 0 20 40 60 80 100 120 Dec 2012 Target Dec 2012 Actual Percentage Source: World Bank staff calculations based on Ministry of Finance data June 2013 | Edition No. 8 9 The State of Kenya’s Economy A key challenge that continues to follow on A new challenge that the budget will face over development expenditure is the low execution the next three years comes from the devolution rate. Poor implementation of the budget affects process. In line with constitutional obligations, service delivery. The overall rate of absorption the government has allocated KES 210 billion has remained the same at 72 percent. Recurrent in 2013/14 to the counties. These funds are to expenditure absorption rate was 90.2 percent cater for devolved functions, such as agriculture, (higher than 84 percent at the end of June 2012), health and infrastructural projects. There are and development expenditure absorption rate two immediate fiscal challenges related to the stood at 45.8 percent (compared to 55 percent at devolution process. First, as Figure 1.14 shows, the end of June 2012). there is wide variance in per capita allocations Figure 1.14: Per capita allocation for counties shows wide variations Per capita share of counties revenue allocations for 2013/2014 Lamu KES 15,255 Isiolo KES 13,178 Marsabit Tana River Samburu Nyeri Taita Turkana Elgeyo/Marakwet Wajir Garissa Embu Mombasa Laikipia West Pokot Kericho Kisumu Tharaka Nithi Baringo Mandera Nyandarua Kirinyanga Homa Bay Kwale Muranga Nairobi Kitui Busia Nakuru Nyamira National Average : KES 4,867 Kisii Vihiga Makueni Migori Kiambu Siaya Kilifi Machakos Nandi Meru Kajiado Uasin Gishu Kakamega Narok Tranzoia Bomet Bungoma KES 3,354 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 2013/2014 County Allocati on per capita in KES Source: World Bank staff calculations based on Ministry of Finance data 10 June 2013 | Edition No. 8 The State of Kenya’s Economy Box 1.2: Macroeconomic implications of devolution The macroeconomic implications of devolution are, in the short-term, primarily fiscal: devolution is likely to be costly, significantly increasing total public expenditure by national and county governments compared to pre- devolution levels. This expansion is likely to be driven by a number of factors: • The proposed county equitable share (vertical allocation) for 2013/14 is significantly larger than the estimated cost of inherited devolved functions in the 2012/13 budget. In the 2012/13 budget, devolved functions to be financed by the county equitable share in future years were identified with a “98� code and amounted to approximately Ksh 130 billion, not including CDF, which the 2013/14 budget indicates will not be devolved. However, the Budget Policy Statement estimates counties should receive 198 billion, including conditional grants. Even allowing for inflation (which the BPS estimates at around 16.3 percent for devolved functions between 2012/13 and 2013/14) this represents a significant increase which is not clearly explained or itemized in the BPS. • Negotiations around revenue sharing in Parliament suggest these amounts may be increased. The Division of Revenue Bill passed by the National Assembly on 9th May increased the allocation to counties to 213 billion (including an equitable share of 190 billion) and the Senate has recommended an equitable share of Ksh 238 billion. • The most obvious way to create the fiscal space needed to pay an increased allocation to counties will be to reprioritize funds away from national programs. In many cases, it may be assumed that counties will now absorb the cost of these programs. If counties are not clearly advised which functions they are expected to fund, the national budget may have to absorb unbudgeted costs of paying for these services in the short term to avoid service collapse. • It is particularly important that counties understand what salary costs they will be responsible for. Allocations to health, agriculture and other sectors in the national budget suggest these salaries of many staff at county level are no longer provided for. • Redistribution of public resources through the new formula (horizontal allocation) for the distribution of the county equitable share is costly. This is because implementing a widely redistributive formula while also maintaining historically privileged counties at the funding levels required to sustain their 2012/13 levels of service delivery requires extra resources. The distribution of the equitable share may leave some counties with relatively little to spend on development. • Devolution creates significant new activities (the cost of county assemblies, county executives, the Senate, etc.), without proposing many immediate efficiency savings (District treasuries will continue to administer the finances of national functions in the counties for example). Counties have yet to have time to explore possible efficiency savings that may in the medium- to long-term mean devolution results in lower total spending per capita in certain counties due to reduced administrative costs. • Possible return of seconded staff to national government by counties that either cannot afford to or do not want to pay the salaries of employees seconded from the national government in support of devolved functions may represent an unbudgeted burden on the national level. • Subnational investment spending-especially for education and health infrastructure that was formerly financed by e.g. the Local Authorities Transfer Fund (LATF)-must now be financed from the county equitable share. Since in some counties the equitable share will not be adequate to meet inherited recurrent costs because of the distributional impact of the formula, and no additional infrastructure grants are being proposed (although the CDF may be maintained as a conditional grant), these counties will either need to suspend investment spending or borrow to finance new infrastructure. • County governments are likely to seek to borrow in order to finance increased expenditures. Although the PFM Act sets clear limits on county borrowing, to be determined by the Intergovernmental Budget and Economic Council (IBEC) and approved by Parliament, these mechanisms are yet to be set up. Anecdotal evidence regarding early draft county budgets for 2013/14 appears to suggest that many draft budgets envisage significant borrowing. If not carefully managed, these expansionary pressures could undermine Kenya’s hard won macroeconomic stability, either by contributing to larger national government fiscal deficits and an increasing debt stock, or by an expansion of subnational borrowing which the national government may be expected to guarantee. Source: World Bank June 2013 | Edition No. 8 11 The State of Kenya’s Economy between counties. Second, the devolution Figure 1.15). M1’s rate of growth increased from formula is different from the previous approach 2.1 percent in July 2012, to 20 percent in April2013, to geographical distribution of spending, and will while M2’s growth increased from 13.9 percent to lead to large variation between what will be spent 18.5 percent in the same period. The growth of and what used to be spent. Some counties will reserve money, the monetary instrument that CBK receive a big boost in funding, while others will face has direct control over, increased from 6.7 percent substantial cuts. Both cases pose fiscal challenges: in October 2012 to 9.5 percent in April 2013. How to absorb spending in the former? And; How to rationalize expenditure in the latter? Figure 1.15: Monetary aggregates started increasing in response to policy easing in the second half of 2012 Growth of monetary aggregates (percent) 1.3 Monetary conditions have eased but 35 policy must keep an eye on inflation while 30 supporting growth 25 A fter an aggressive and successful tightening, Growth (Percent) 20 the Central Bank of Kenya has reversed gear, 15 now that inflation is under control, and cut its 10 policy rates to forestall a prolonged economic 5 slowdown. The CBK’s action to tighten monetary 0 policy in order to fight inflation and stabilize the Apr-09 Apr-10 Apr-11 Apr-12 Apr-13 Oct-09 Oct-10 Oct-11 Oct-12 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jul-09 Jul-10 Jul-11 Jul-12 -5 exchange rate, triggered a climb in interest rates, Reserve Money M0 M1 M2 M3 which in turn cooled off the economy. As inflation Source: World Bank Staff calculations based on CBK data has come down below the targeted 5 percent, and Notes: Interest rates have been transformed by taking a three month moving average used with inflation expectations anchored at a lower level, the CBK reduced the central bank rate (CBR) by 950 basis points, signaling the market to lower The money market rates have also reacted to lending rates and ease credit conditions. the monetary easing. Short term money market interest rates have declined in response to CBK’s Monetary aggregates are increasing in response to eased monetary policy. By April 2013, in response the policy easing. Following a significant reduction to the 9.5 percentage points reduction in the CBR, in the growth of monetary aggregates in the first the 91 day Treasury bill and the interbank rates half of 2012, monetary aggregates have started had declined by 21 and 10.2 percentage points, to increase to reflect CBK’s monetary easing (see respectively from their peak of 28.9 percent Figure 1.16: Short term rates have responded to monetary policy which seems effective Short terms rates have responded to monetary policy and calm 40 Monetay aggregate growth (percent) in the market restored Short term interest rates (percent) 35 35 30 30 25 25 25 20 20 20 15 Percent 15 15 10 10 5 10 5 0 5 -5 0 0 -10 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Jan-01 Aug-01 Mar-02 Oct-02 May-03 Dec-03 Jul-04 Feb-05 Sep-05 Apr-06 Nov-06 Jun-07 Jan-08 Aug-08 Mar-09 Oct-09 May-10 Dec-10 Jul-11 Feb=12 Sep-12 Apr-13 Interbank 91-Tbill CBR Interbank 91-Tbill CBR M1 Source: World Bank Staff calculations based on CBK data Notes: Interest rates have been transformed by taking a three month moving average used 12 June 2013 | Edition No. 8 The State of Kenya’s Economy Figure 1.17: Long term rates have remained high but are declining with monetary easing 25 Business loans became more expensive with tighter monetary policy 25 20.28 23 20 17.83 21 19.47 19 17.83 15 17 16.97 Percent 11.42 Percent 8.49 15 10 13 1.66 6.41 11 5 9 1.49 7 0 5 Apr-13 Apr-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Jul-11 Apr-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Apr-10 Oct-10 Jan-11 Jul-11 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Jul-10 -5 Corporate lending rate Business lending rate Overall Weighted Lending Rate Average deposit rate Savings Interesr rate spread Personal lending rate Overall weighted lending rate Source: World Bank Staff calculations based on CBK data. Notes: Interest rates have been transformed by taking a three month moving average used (November 2011) and 20.56 percent (January 2012 to 17.9 percent in April2013 (see Figure 1.17). 2012)( see figure 1.16). In addition, unlike what Average rates paid on three month term deposits happened during the monetary policy tightening also declined from 8.25 percent in July 2012 to period when the interbank rates rose above the 6.4 percent in April2013. Savings rates remained Treasury bill rates, Treasury bill rates are now unchanged (see Figure 1.17).7 As a result, interest above interbank rates. rate spread (lending minus deposit rate) remained high at 11.5 percent, compared to the pre-tight Long term interest rates are falling, but at a much monetary policy level of 10.3 percent. slower rate. While commercial banks were quick to increase their lending rates as CBK tightened Business loans bore the brunt of tight monetary monetary policy, they have not reacted with policy. Commercial banks are pricing business similar vigor in reducing their lending rates to loans more expensively than personal/household their customers during the monetary easing. The loans, while corporate loans are priced less average weighted lending rates have only declined expensively. Before the tight monetary policy by 243 basis points in response to 950 basis points begun in the last quarter of 2011, commercial reduction in the CBR. Overall, the weighted lending banks priced personal/household loans more rates declined from a peak of 20.3 percent in June expensively than business and corporate loans. Figure 1.18: Commercial banks pricing behavior of loans seems to vary across categories of borrowers Corporate loans: overdraft loans most expensive while 1-5 year Business loans: overdraft loans least expensive, 1-5 year tenor loans least expensive on average more expensive on average 22 24 20 22 20 18 18 Percent Percent 16 16 14 14 12 12 10 10 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Average weighted corporate Overdraft/loan 1-5 years Over 5 years Average weighted business loans overdraft/loan 1-5years over 5years Source: World Bank Staff calculations based on CBK data. Notes: Interest rates have been transformed by taking a three month moving average used 7 Savings deposits are non-term. June 2013 | Edition No. 8 13 The State of Kenya’s Economy However, this changed with the onset of tight Term deposits increased significantly as monetary policy conditions (see Figure 1.18). By commercial banks competed for deposits to April 2013, average lending rates on business loans meet statutory liquidity requirements when CBK were priced at 250 basis points above corporate tightened its lending conditions. Average deposit loans, 162 basis points above personal loans. This rates for all commercial banks increased by 500 signals the risk by which banks viewed businesses basis points from 3.50 percent in May 2011 to a in Kenya during an economic downturn and the peak of 8.49 percent in May 2012, before falling election period. Another interesting observation to 6.41 percent in April 2013. This significant is the pricing of loans based on tenor.8 While increase was driven by 0-3 month term deposits banks tended to price corporate overdrafts loans category, which increased by almost 900 basis more expensively compared to 1-5 years tenor, points from 3.96 percent to 12.87 percent, before the opposite was true with business loans, where falling to 8.51 percent in the same period. At the overdrafts were priced more cheaply compared same time, term deposits greater than 3 months with loans of 1-5 years tenor. increased by 560 basis points from 5.17 percent to Figure 1.19: Commercial banks offered high deposit rates for 10.96 percent, before falling to 9.21 percent. term deposits to attract more liquidity 14 12.88 The private sector suffered a massive credit 12 10.96 squeeze in 2012. Because of tight monetary 10 9.21 policy during the first half of 2012, credit to the 8.49 private sector dropped by KES 153.2 billion (a 56 Percent 8 8.51 6 5.15 6.41 percent drop) as commercial banks only disbursed 4 3.96 3.50 KES 121.1 billion in 2012, compared to KES 274.3 2 billion in 2011. The growth of the credit to the 0 private sector declined from 30.9 percent in 2011, to 10.4 percent in 2012. There was a significant Apr-12 Apr-13 Apr-10 Apr-11 Oct-11 Jan-12 Jul-12 Oct-12 Jan-13 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Jul-10 Oct-10 Jan-11 Jul-11 cutback across all sectors of the economy except Average deposit rate Demand 0-3 months Over 3 months Savings building and construction, which received KES 0.2 Source: Central Bank of Kenya, World Bank Staff calculations billion more credit in 2012 compared to 2011. Notes: Interest rates have been transformed by taking a three month moving average used In terms of the amount of credit, transport and communication suffered a KES 39 billion cutback Commercial banks offered high deposit rates to in credit in 2012, when compared to 2011, private counteract CBK’s measures. households (KES 26.8 billion), trade (KES 17 billion) Figure 1.20: Tight monetary policy constrained credit to all sectors of the economy Private sector growth by sector (Percent) Private sector Credit distribution by sector (KES Billion) Transport and Communication 3.4 Transport and Communication 27 Mining and Quarrying 1.6 Mining and Quarrying 11 Finance and Insurance 0.2 0.8 3 7 Finance and Insurance Agriculture 0.4 1.3 4 Agriculture 11 Business Services 0.5 Business Services 6 Consumer Durables 0.6 1.7 7 Consumer Durables 15 Private Households 1.1 4.6 13 Other Ac tivities 39 Other Ac tivities 1.1 13 5.0 Private Households 40 Trade 1.7 4.0 18 Building and Construc tion 18 Building and Construc tion 1.9 2.4 20 Trade 37 Manufacturing 2.1 3.8 23 Manufacturing 34 Real Estate 2.3 4.6 25 Real estate 39 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 -20 -10 - 10 20 30 40 50 Weighted growth rate (Percent) KES Billion Dec-12 Dec-11 Dec-12 Dec-11 Source: World Bank Staff calculations based on CBK data 8 Tenor refers to the length of the loan period. 14 June 2013 | Edition No. 8 The State of Kenya’s Economy and real estate (KES 14 billion). The top four credit 2011, to 2.3 trillion (67.7 percent of GDP) in recipients of in 2012 were real estate (20 percent), December 2012. The main components of the manufacturing (19 percent), trade (17 percent), banking sector’s assets comprised of loans and and building and construction (15 percent). This advances (55.6 percent), government securities was a slight change from 2011, when the top four (17.7 percent) and placements (6.2 percent). top credit recipients were private households Despite prevailing high interest rates, the quality (14.7 percent), real estate (14.1 percent), trade of banking assets remained resilient. The stock of (13.6 percent) and manufacturing (12.4 percent). gross non-performing loans (NPLs) increased by 16.9 percent, from KES 53 billion in 2011, to KES However, the rate of private sector credit growth 62 billion in 2012. As a result, the quality of assets has been picking up gradually, as bank lending deteriorated marginally. Asset quality measured as conditions eased slowly in 2013. In the first four a proportion of net NPLs to gross loans deteriorated months of 2013, commercial banks have loaned from 1.2 percent to 1.7 percent, while the ratio out KES 42.3 billion, compared to KES 38.1 billion of gross NPLs to gross loans increased from 4.4 in 2012 (11 percent increase). Majority of the loans percent to 4.7 percent over the same period.9 A have been to private households (33.9 percent), significant portion (67 percent) of the NPLs was in business loans (33.8 percent) and domestic trade personal household category (33.2 percent), trade (15.3 percent). Credit and activity are propelling (22.1 percent) and real estate (11.6 percent). each other. Credit expansion has continued at an elevated pace, and credit-to-GDP ratios have There has been a broad market rally at the NSE continued to move up. However, private sector indicating a reacceleration in activity. The equities credit remains sluggish, compared to pre-crisis market is booming with equity prices rising level. If the current CBK’s monetary policy stance strongly. The NSE equity index is up 44 percent of monetary easing continues, it is expected to in the twelve month period through March 2013, driven by strong performances across all sectors Figure 1.21: Kenya’s equities are recovering strongly of the market. Kenya’s equities have continued 7,000 16,000 to follow the global equity markets into higher 6,000 14,000 territory in 2013. In the first 5 months of 2013, the 5,000 12,000 NSE has increased by 873 points (21.2 percent), while the Dow Jones Industrial Average increased Dow Jones 10,000 NSE Index 4,000 3,000 8,000 by 2011 points (15.3 percent). 6,000 2,000 4,000 1.4 The External Sector poses challenges and 1,000 2,000 risks to Kenya’s growth prospects 0 0 K Dec-06 Apr-07 Aug-07 Dec-07 Apr-08 Aug-08 Dec-08 Apr-09 Aug-09 Dec-09 Apr-10 Aug-10 Dec-10 Apr-11 Aug-11 Dec-11 Apr-12 Aug-12 Dec-12 Apr-13 enya’s “external growth engine� remains NSE Index, End-month Dow Jones stifled signaling stagnation or loss in Source: World Bank Staff calculations based on NSE and Bloomberg data competitiveness. Kenya’s exports as share of GDP have remained constant since 2005 (at around 23- translate slowly into more dynamic bank lending. 24 percent) while imports have ballooned from 32 The banking sector remained healthy in 2012, percent in 2005, to 40 percent of GDP in 2012. The despite an environment of tight monetary policy. exports to imports ratio has declined. For every Aggregate balance sheets grew by 15 percent in US$ 100 worth of imports, exports could pay US$ 2012. The banking sector’s total assets grew from 77 of that in 2005. However, this has reduced to 2.0 trillion (66.3 percent of GDP) in December US$ 58. Appreciation in the real exchange rate is June 2013 | Edition No. 8 15 The State of Kenya’s Economy an important contributor to the export stagnation. on the back of strong net short term flows and The shilling has appreciated by 33 percent, or 3 project loans (including defense loans). The overall percent per annum, in real terms since 2003.10 balance of payments improved from a deficit of Though the nominal exchange rate depreciated US$ 0.04 billion (0.1 percent) in 2011 to a surplus during this period, Kenya had higher inflation than of US$ 1.4 b (3.0 percent of GDP) in 2012, mainly its major trading partners, which in turn led to real the result of an increase in project loans from US$ appreciation, i.e. loss in competitiveness. 0.6 b to US$ 1.4 billion. The basic balance (current account balance plus net direct investment)11 The current account continued to deteriorate in remained negative, implying a continued reliance 2012. The current account deficit widened from on potentially volatile portfolio investment, which US$ 3.3 billion (9.7 percent of GDP) in 2011, to signals that Kenya continues to remain vulnerable US$ 4.5 billion in 2012 (11.1 percent of GDP). to external shocks (see Figure 1.22). This reflected the combination of subdued export Figure 1.22: A wider current account deficit is being financed demand from Kenya’s trading partners in Europe, by short term flows and strong import demand fueled by the growth Balance of payment flows, US$ billion 8.0 in capital imports. 6.0 2.1 Kenya’s current account has deteriorated sharply 4.0 0.4 1.4 as exports have stagnated, while imports US$ billion 2.0 2.9 3.7 3.7 increased. The share of Kenya’s exports in GDP has 0.0 remained constant since 2005, while the share of 2005 2006 2007 2008 2009 2010 2011 2012 2013 -2.0 (Apr) Kenya’s imports has increased. Specifically, exports -3.3 -4.5 -4.4 marginally declined from 24.3 percent in 2005, to -4.0 23.1 percent of GDP in 2012, while at the same -6.0 Current Account Net short term flows (including E&O) time imports as share of GDP increased from 32 Net other capital Overall Balance percent to 40 percent. Though a significant portion Source: World Bank Staff calculations based on CBK data of the increase of imports can be attributed to the oil bill and increased imports of machinery, The balance of trade deteriorated further in transport goods and other intermediate goods, 2012, reflecting strong capital import demand the appreciation in the real exchange (discussed coupled with weak commodity exports. The non- below) has contributed to the problem. More oil trade balance deteriorated slightly to US$ -2.8b recently, the deterioration in the current account (6.91 percent of GDP) in 2012, from US$ -2.4b reflected an increase in non-oil imports by 13.8 (6.89 percent of GDP) in 2011, while the deficit in percent to US$ 12.2b in 2012, from US$ 10.7 in the balance of trade including oil increased to US$ 2011. Crude oil imports were not a major factor 6.9b (16.9 percent of GDP), from US$ 6.4b (18.8 driving the current account deficit in 2012, as percent of GDP) in the same period (see Figure the oil bill remained the same as in 2011 at US$ 1.23). Turning to a more detailed view of recent 4.1 billion. Capital imports were a major factor, trade dynamics, in 2012 the merchandise account increasing by 29 percent from US$ 3.7billion to deficit increased from US$ 9b (26.8 percent of US$ 4.9 billion. GDP) in 2011, to US$ 10.4b (25.1 percent of GDP) in 2012. Total imports growth moderated from a The overall balance of payments returned to growth of 19.5 percent in 2011, to 10.0 percent in surplus in 2012. The overall balance moved from 2012, to reach US$ 16.3b (40.3 percent of GDP). a small deficit in 2011 to a large surplus in 2012, Capital goods imports grew by 29 percent from 10 Nominal exchange rate is the amount of Kenyan shillings that can purchase a unit of a given foreign currency (e.g. a US dollar). A decrease in this variable is termed nominal appreciation of the currency while an increase is termed nominal depreciation of the currency. Real exchange rates are nominal exchange rate that has been adjusted for the difference in inflation between Kenya and its trading partners. 16 June 2013 | Edition No. 8 The State of Kenya’s Economy US$ 3.7b to US$ 4.9b, while imports of crude Figure 1.23: Non-oil imports mainly capital imports increased significantly in 2012 while oil imports remained flat oil remained flat in 2012 at US$ 4.08b, same as Exports, imports and trade balances, nominal US$ billions in 2011. Exports of goods grew by 5.5 percent 15 to reach US$ 6.2b (15.1 percent), from US$ 5.8b 10 (17.3 percent of GDP) in the same period. This 2.6 3.3 3.5 5 underperformance of exports is explained by poor 5.8 6.1 6.1 US$ billion performance in Kenya’s main export crops. Tea and 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 -4.1 (Apr) horticulture grew by only 4 percent and 2 percent -5 -4.1 respectively in 2012. Strong performance in the -10 -10.7 -12.2 -12.4 transportation account and other government -15 services saw non-factor services grow 27 by -20 percent in 2012, from US$ 2.6billion to US$ 3.2 Exports (fob) Export NFS Trade balance Non-oil imports Oil imports Trade balance (non-oil) billion. Overall exports of goods and services for Source: World Bank Staff calculations based on CBK data 2012 increased by 12.2 percent to reach US$ 9.4b (23.2 percent of GDP), up from US$ 8.4b (24.9 178.1 million (2010) and US$ 335 million (2011) percent of GDP), in nominal USD terms. in FDI. Kenya’s performance in attracting foreign investment remains limited compared to its peers. Despite its potential, Kenya is still not attracting The FDI Kenya attracted was only equivalent to adequate long term capital inflows to power 0.8 percent of its GDP in 2010-11, compared its growth. Kenya receives less long term capital to Rwanda (1.2 percent of GDP), Tanzania (2.8 inflows than any other country in the EAC region. percent of GDP), and Uganda (6.2 percent of GDP) According to CBK balance of payment data, official in the same period (see Figure 1.24). However, medium and long terms flows, which are mainly following the recent peaceful elections, and given project loans (including defense loans), increased the improvements in the governance framework from US$ 527 million in 2010, to US$ 612 million since the new Constitution was adopted in 2010, in 2011, and then to US$ 1,449 million in 2012. FDI to Kenya is expected to increase in the future. However, foreign direct investment (FDI) remained subdued, as Kenya received only US$ 177 million Kenya has not been an attractive destination of (2010), US$ 140 million (2011) and US$ 164 million FDI. A variety of factors explain low FDI in Kenya (i) (2012) according to CBK data. UNCTAD data on infrastructure bottlenecks both in energy and roads the other hand shows that Kenya received US$ have been a major constraint on FDI. For Kenya Figure 1.24: FDI inflows into Kenya remains low compared to its peers in the region while Short term flows have increased significantly Inbound FDI as a percentage share of GDP Capital Inflows as percentage share of GDP 10 16 9.0 14 13.5 8 12 Percent of GDP 10.5 6.1 Percent 10 6 9.4 8 6 4 2.8 4 6.2 2 2 0.7 1.0 0.9 2.8 0.5 0.3 0 0 0.4 1.2 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 0.8 -2 Kenya Ethiopia Rwanda Tanzania Uganda Madagascar Zambia Mozambique Short Term and Net Errors & Omissions (NEO) Short Term (incl. portfolio flows) 1990/99 2000/09 2010/11 FDI (Source CBK) FDI (source UNCTAD) Source: World Bank Staff calculations based on CBK and UNCTAD data 11 Even if we defined basic balance as current account deficit plus non short term capital, it still remains negative, further demonstrating Kenya’s vulnerability. June 2013 | Edition No. 8 17 The State of Kenya’s Economy to be an attractive destination for FDI, it requires the onset of global financial crisis in 2008. Strong infrastructure to facilitate the production activities net portfolio investment inflows were in line with and sale of goods and services. Good infrastructure a solid domestic government bond and stock lowers the transaction costs, which enable market performance, which occurred at a time investors to earn returns on their investments, as when the global risk appetite was improving, and their enterprises are able to generate profits. This interest rates in advanced and emerging markets constraint has been recognized by the government were low. as a larger proportion of Kenya’s budget is now allocated to roads and energy sector. (ii) Kenya’s Figure 1.25: Short term flows has become a significant factor in the balance of payment labor productivity has been falling in the recent 70 past, while at the same time, labour costs have 64.9 59.6 65.3 63.5 60 been rising fast compared to their productivity. 50 (iii) The regulatory environment in Kenya has 40 been hostile to FDI and impeded it. Excessive 31.1 Percent 30 regulations have hindered entrepreneurial 25.4 20 activity, as firms spend more time and resources 10.6 7.5 10 complying with rules and regulations. The long 0.9 0 delays in resolving disputes in the judiciary and Short Term Flows Short Term Flows Short Term Flows Short Term Flows -1.4 Foreign direct -10 other cumbersome compliance items, have and Net Errors and Net Errors and and Omissions/ Omissions/ and Net Errors and Net Errors and and Omissions/ Omissions/Capital Investment/ Exports of Goods Exports of Services Merchandise Exports of Goods and Financial and Services discouraged FDI. In addition, the regulations that Exports and services account require foreign firms to enter into mandatory joint 2002 2012 ventures partnerships (30 percent share) with Source: World Bank Staff calculations based on CBK data locals in order to invest in Kenya, makes it a less favorable investment destination. Kenya remains exposed to a reduction in shorter- term capital flows in the event of heightened Short term flows continue to dominate Kenya’s uncertainty on global financial markets. The balance of payments, and expose the economy’s current account deficit is expected to widen vulnerability to sudden reversals. Short term further, as the economy expands, after the flows including errors and omissions increased general elections. Moreover, imports of heavy from US$ 2 billion (6.1 percent of GDP) in 2010 machinery and transportation equipment, which to US$ 4.3 billion (9 percent of GDP) in 2012. are important for infrastructure projects and Excluding the errors and omissions, short terms oil and gas exploration, are expected to grow in flows increased from US$ 1.1 billion (3.5 percent of 2013. Kenya runs a risk, if it continues to depend GDP) to US$ 2.4 billion (5.8 percent of GDP) in the on short term flows that uncertainties in global same period. The critical importance of short term financial markets could undermine its ability to flows in financing the current account is detected finance the current account deficit. Kenya would when its contribution to the capital and financial benefit from increased long term flows and FDI, account is analyzed (see Figure 1.25). Short term to substitute for some of the short term flows that flows (including errors and omissions) constitute are more vulnerable to uncertainties in the global 63.5 percent of net capital and financial account. capital markets. This was a significant decline from 87.9 percent in 2011, which is accounted for by US$ 1.2 billion The Central Bank of Kenya has built a large in the project loans (including defense loans) enough buffer to cushion the economy in the that were recorded in 2012. Portfolio inflows are event of external shock. The CBK increased its mostly for investment in the equities and bond holding of international reserves by US$ 1.5 billion markets, where returns have been very high since (36 percent) in 2012, from US$ 4.2b in 2011 to 18 June 2013 | Edition No. 8 The State of Kenya’s Economy US$ 5.7b. The import cover increased from 3.7in Exports remained weak in 2012. Weak external December 2011 to 4.3 in 2012, which is above the demand for Kenya’s exports was as a result of statutory requirement of 4 months. subdued global demand, and lower commodity prices. Tea remains the major contributor to Kenya’s structure of exports and imports has merchandise exports earnings, bringing in about 20 not changed in the last 5 years. Machinery and percent of total earnings, followed by horticulture transport equipment constitute the largest share and manufactured goods, which each contributed of imports accounting for about 30 percent, while about 11 percent in 2012. While tea earnings oil imports take a 25 percent share. Variations in oil have increased from 17 percent in 2007 to 20 imports are driven by price fluctuations. Imports percent in 2012, export earnings have declined for of machinery and transport equipment tend to horticulture from 15 percent in 2007 to 11 percent expand the economy’s productive capacity, and in 2012, in the face of subdued demand in Europe. are beneficial to Kenya’s long term growth (see The largest destination of Kenya’s exports is Africa, Table 1.2). where 48 percent of its exports go with 26 percent Table 1.2: Kenya’s top exports and imports by broad functional category Selected Exports 2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012 Coffee (%) 4 3 4 4 4 4 0.6 0.5 0.7 0.6 0.7 0.7 Tea (%) 17 18 20 22 20 20 2.5 3.0 2.9 3.6 3.4 3.0 Horticulture (%) 15 15 15 14 12 11 2.2 2.5 2.3 2.3 2.0 1.7 Manufactured Goods (%) 12 12 12 12 13 11 0.6 0.4 0.3 0.3 0.4 0.2 Selected Imports (%) As a share of Total imports As a share of GDP Oil (%) 21 27 21 22 28 25 7.0 10.0 7.2 8.3 12.1 10.1 Chemicals (%) 13 13 13 13 13 13 4.2 4.7 4.3 5.0 5.8 5.1 Manufactured Goods (%) 16 14 14 14 15 14 5.3 5.2 4.6 5.5 6.7 5.7 Machinery & Transport 31 27 30 31 25 29 10.3 10.1 10.0 11.8 11.0 11.7 Equipment (%) Selected Exports (%) As a share of Total Exports As a share of GDP 2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012 Coffee 4.03 3.07 4.45 3.99 3.82 4.39 0.61 0.51 0.66 0.65 0.66 0.66 Tea 16.76 18.30 19.69 22.17 19.86 19.57 2.54 3.03 2.92 3.60 3.43 2.97 Horticulture 14.70 15.12 15.29 13.88 11.67 11.34 2.23 2.51 2.26 2.25 2.02 1.72 Manufactured Goods 12.42 12.38 11.62 11.63 12.55 11.43 0.59 0.38 0.31 0.28 0.35 0.20 Selected Imports (%) As a share of Total imports As a share of GDP Oil 21.17 26.55 21.29 21.56 27.55 25.05 7.05 10.02 7.17 8.30 12.14 10.09 Chemicals 12.74 12.58 12.86 12.93 13.15 12.74 4.24 4.75 4.33 4.98 5.79 5.13 Manufactured Goods 15.83 13.83 13.70 14.31 15.19 14.13 5.27 5.22 4.61 5.51 6.69 5.69 Machinery & Transport 30.87 26.65 29.77 30.72 24.88 29.15 10.28 10.05 10.02 11.83 10.96 11.74 Equipment Source: World Bank Staff calculations based on CBK data June 2013 | Edition No. 8 19 The State of Kenya’s Economy Table 1.3: Selected Kenya’s Trading Partners Top 10 Exports Destination Top 10 Origin of Imports (country of origin Percent of Percent of US$ (Million) US$ (Million) Total Exports Total Exports 1. Uganda 67,450 13.0 India 195,230 14.0 2. Tanzania 46,036 8.9 China 167,206 12.2 3. UK 40,630 7.8 UAE 149,879 10.9 4. Netherlands 31,056 6.0 Saudi Arabia 66,841 4.9 5. UAE 28,608 5.5 USA 65,966 4.8 6. USA 26,405 5.1 Japan 63,135 4.6 7. Pakistan 23,889 4.6 South Africa 61,954 4.5 8. Egypt 21,464 4.1 Indonesia 55,241 4.0 Rwanda 16,151 3.1 UK 43,849 3.2 9. Germany 9,771 1.9 Germany 41,474 3.0 Total 517,847 60.1 1,374,587 66.3 1. Africa 250,589 48.4 Asia 856,525 62.3 2. Europe 125,195 24.2 Middle East 284,117 20.7 3. Asia 105,460 20.4 Far East 572,408 41.6 4. Middle east 42,065 8.1 Europe 249,769 18.2 5. Far East 63,395 12.2 Africa 140,755 10.2 6. EAC 134,946 26.1 EAC 30,857 2.2 7. COMESA 175,732 33.9 COMESA 61,572 4.5 Source: KNBS, Economic Survey 2013 going to East African Community. The EU is Kenya’s long term perspective, between January 2003 second main trading partner and accounts for 24 and December 2012, the shilling has depreciated percent of it’s exports, of which 7.8 percent goes by 11.8 percent, 10.5 percent and 39.8 percent, to the UK. As such, even though the economic respectively against the US$, the Sterling Pound situation in Europe affects Kenya’s exports, more and the Euro. As such, the average annual rate than three quarters of its trading partners have of depreciation was 1.2 percent, 1.1 percent and not seen an economic crisis similar to what has 4 percent per annum, respectively in the period. been happening in Europe in the past few years. The rate of depreciation has been much lower when compared to Kenya’s inflation rate during The exchange rate stabilized in 2012. The Kenya the period, which averaged 9.6 percent per annum shilling in December 2012 traded at KES 85.99 in the last 10 years. Between December 2012 and against the US$ (compared with KES 86.66 in April 2013, the shilling had appreciated in nominal December 2011). This represented a 0.8 percent terms by 2.1, 7.2 and 2.8 percent against the US nominal appreciation against the US$. Taking a Dollar, the Sterling Pound and the Euro mainly on 14 Nominal exchange rate can be defined as the amount of Kenya shillings that can purchase a unit of a given foreign currency. A decrease in this variable is termed nominal appreciation of the currency while an increase is termed nominal depreciation of the currency. Real exchange rates are nominal exchange rate that has been adjusted for the different rates of inflation between Kenya shillings and a foreign currency. In practice, changes of the real exchange rate rather than its absolute level are important. An increase in the real exchange rate is termed depreciation while a decrease is depreciation. The importance stems from the fact that it can be used as an indicator of competitiveness in the foreign trade of a country. 15 Long-run equilibrium real exchange rate is the real rate that, for given values of “economic fundamentals� (openness, productivity differentials, terms of trade, public expenditure, direct foreign investment, international interest rates, etc.) is compatible with simultaneous achievement of internal and external equilibrium. For methods to estimate long-run real equilibrium exchange rate, see Hinkle and others 1999. 20 June 2013 | Edition No. 8 The State of Kenya’s Economy Box 1.3: Could the deprecation of the Kenyan shilling be beneficial to Kenya’s economy? Evidence from the literature Recent studies on Kenya have shown that import price elasticity of demand is greater than unity. This implies that import demand in Kenya is fairly elastic. In other words, as relative prices fall import demand will increase by a greater than proportional amount. Aggregate price elasticity of demand estimates for Kenya13 Jones Tokarick Kee et al Faini (1988) Senhadji Bruce/Ndii (2003) (2010) (2008) (1977) (1994) Aggregate Import price Elasticity -1.148 -1.33 -1.14 -1.48 -1.45 Even though Kenya’s aggregate price elasticity of demand indicates that imports would respond to the shillings depreciation, there is a very wide disparity across sectors as depicted in Table 2. From the studies by Jones (2003), 10 percent depreciation of the shilling would lead to 28.2 percent reduction in rubber and hinds, 24 percent in Footwear and headgear, 20 percent in precision instruments etc. However, capital goods, chemicals and vehicles and transport would fall less than proportionately Import price elasticity of demand for Kenya sectors Sector Sector Elasticity Sector Sector Elasticity 1. Rubber and Hides -2.82 9. Mineral Products -1.32 2. Footwear and Headgear etc -2.42 10. stones, Pearls Glass etc -1.29 3. Precision Instruments -2.01 11. Beverages and Tobacco -0.99 4. Base metals -1.93 12. Miscellaneous Manufactures -0.95 5. Textiles and Garments -1.76 13. Vehicles and Transport Equipment -0.88 6. Live animals -1.58 14. Arms and Munitions -0.63 7. Wood and Paper Products -1.54 15. Chemicals -0.53 8. Vegetable products -1.51 16. Machinery and Electrical Equipment 0.22 Source: Jones C. (2003) The response of exports to depreciation would be weak in the short run. According to recent studies Kenya’s export supply elasticities are in the range of 0.28-0.60 in the short run and 0.33 -0.88 in the long run (see Broda et al (2008) and Tovarick (2010). If these elastcitites are correct, then a 10 percent depreciation will elicit less than proportionate export supply response. Lastly, using import demand and export supply elasticities from various studies, Tovarick (2010) calculated Kenya’s trade balance elaticities to range from 0.46 in domestic currency and 0.56 in foreign currency. According to this study, a 10 percent depreciation of the shilling will improve the trade balance by 4.6 percent in domestic currency terms or 5.6 percent in foreign currency terms. Source: World Bank 13 Kee, H. L. Nicita A., and Olarreaga (2008): Import Demand Elastcities and Trade distortions� Review of Economics and Statistics Vol 90, No4, pp 666- 682 Senhadji, Semlali 1997: Time series of Structural Import demand Equations- A cross country Analysis, IMF Working Paper WP/97/132 Jones C (2003): Aggregate and Sector Import Price Elastcities for a sample of African Countries’ CREDIT Research Paper No 08/03 Tokarick S (2010): A Method for Calculating Export Supply and Import Demand Elasticities, IMF Working Paper WP/10/108. June 2013 | Edition No. 8 21 The State of Kenya’s Economy Fig 1.26: The exchange rate stabilized in 2012 but has depreciated at the rate of 1-4 percent per year in nominal terms in the last 10 years against the major currencies 180.00 180.0 2003 and December 30, 2013 the nominal depreciati on of the shilling was 11.8%, 10.5% and 39.8% against the US dolar, the UK sterling pound and the EURO. Nominalised Exchange rate Jan 2003=100 160.00 Kenya Shiling vs other currencies *Between Jan, 138.80 160.0 * The Kenya shilling has lost 40% of its value against 140.00 the euro, and about 10% gainst the sterling and the US dollar 120.00 115.47 139.8 140.0 100.00 86.90 120.0 80.00 111.8 60.00 100.0 110.5 40.00 80.0 20.00 0.00 60.0 Jan-03 May-03 Sep-03 Jan-04 May-04 Sep-04 Jan-05 May-05 Sep-05 Jan-06 May-06 Sep-06 Jan-07 May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09 May-09 Sep-09 Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-12 May-12 Sep-12 Jan-13 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 Jan-12 Jul-12 Jan-13 United States dollar Sterling pound Euro United States dollar Sterling pound Euro Source: World Bank Staff calculations based on CBK data account of peaceful transition of power which The high inflation in 2011 caused a big real had reduced political risk. appreciation of the shilling. The real effective exchange rate (REER) appreciated by 7.8 percent On the other hand, the persistently higher in 2011, and by 14.9 percent between the end of inflation vis-à-vis its trading partners, points to an September and the end of December 2011. Up to appreciation in the real exchange rate, i.e. erosion 2011, the REER had been relatively stable for about in Kenya’s competitiveness. The evolution of the 4 years and appeared to be in line with economic real exchange rate over the last decade, shows a fundamentals. IMF’s Article IV report of January trend of real appreciation. Between January 2003 which looked at data up to the end of Q3 of 2011 and April 2013, the Kenya shilling appreciated by found “no significant evidence of exchange rate 37 percent in real terms, cumulatively representing misalignment�. This assessment was made using an annual appreciation of about three percent.12 three model approaches: the macroeconomic 13 Hence, despite the nominal depreciation of balance, the external sustainability, and the the shilling, Kenya’s inflation was higher than in equilibrium real exchange rate assessment. partner countries, which, in turn, implies that However, between the end of September 2011 the competitiveness of Kenya’s export products (latest data used in the Article IV) and January eroded relative to the domestically produced 2013, the REER appreciated by 21.5 percent, which products in those countries. points to weakened export competitiveness. Figure 1.27: Kenya competitiveness continues to be eroded Trade weighted Exchange rate Jan 2003=100 Depreciati on or appreciati on of the trade weighted exchange rate percent 140.00 * the trade weighted real exchange rate has appreciated by 33% between January 30 2003 and December 2012. The average annual rate is 3.3% per annum * the trade weighted nominal exchange rate has depreciated by 18% between 25 Depreciation of the exchange rate 120.00 January 2003 and December 2012. The average annual rate is 1.8% per annum 20 Jan 2003=100 114 (14%) 15 100.00 10 Percent 5 80.00 0 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-08 Jul-08 Oct-08 Jan-09 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-04 Jul-04 Oct-04 Jan-05 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 -5 60.00 63 (37%) -10 40.00 -15 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 -20 Appreciation of the Exchange rate -25 NEER REER NEER REER Source: World Bank Staff calculations based on CBK data 22 June 2013 | Edition No. 8 The State of Kenya’s Economy Kenya is experiencing a boom in remittances, 2010 (see Figure 1.28). North America is the main surpassing US$ 1 billion for the first time in 2012. source of the remittances (48 percent) followed Remittances increased from US $ 642 million (2.0 by Europe (28 percent) and the rest of the world percent of GDP) in 2010 to US$ 891 million (2.65 (24 percent). The recent increase in remittances is percent of GDP) in 2011, to US$ 1.2 million (2.9 explained both by better data collection methods percent of GDP) in 2012. Monthly average inflows employed by the CBK, and by the ease by which have increased by 82 percent in just 2 years to the diaspora can now send remittances to Kenya US$ 97.6 million in 2012, from US$ 54 million in via commercial banks for investment purposes. Figure 1.28: Remittances have risen sharply in the last few years 120 1400 3.5 100 1200 3.0 1000 2.5 Percent of GDP 80 US $ Million US$ Million 800 2.0 60 600 1.5 40 400 1.0 20 200 0.5 0 0 0 Jan-04 Jun-04 Nov-04 Apr-05 Sep-05 Feb-06 Jul-06 Dec-06 May-07 Oct-07 Mar-08 Aug-08 Jan-09 Jun-09 Nov-09 Apr-10 Sep-10 Feb-11 Jul-11 Dec-11 May-12 Oct-12 Mar-13 Jan-04 Jun-04 Nov-04 Apr-05 Sep-05 Feb-06 Jul-06 Dec-06 May-07 Oct-07 Mar-08 Aug-08 Jan-09 Jun-09 Nov-09 Apr-10 Sep-10 Feb-11 Jul-11 Dec-11 May-12 Oct-12 Mar-13 Monthly 12-months average 12-month cumulative As % of GDP World Bank Staff calculations based on CBK data June 2013 | Edition No. 8 23 The State of Kenya’s Economy 2. Growth Outlook for 2013-2014 2.1 Growth prospects average growth of 4.9 percent (5.6 percent for oil importing countries), but continues to be Kenya’s economic prospects have improved lower than other EAC member states, which are following the peaceful elections in March 2013, estimated to grow at 6.1 percent in 2013. and subsequent transfer of power. The World Bank forecasts growth to reach 5.7 percent in In a high case scenario, Kenya’s GDP growth is 2013 and 6 percent in 2014. This will be the projected to reach 6.1 percent in 2013. Under highest growth since 2007, when the economy this scenario, investment outturns are much grew by 7 percent. Aggregate demand fueled by stronger than in the baseline, due to stronger than strong consumption and investment growth will expected inflows of foreign direct investment, power the economy forward. Growth in the first and ongoing peace dividends. Macros stability half of 2013 was subdued due to election jitters, is sustained, with agriculture harvests being when activity stalled by a wait-and-see attitude favorable, thereby supporting household incomes. to new investment. Starting in the second half of Much of the increased investments is also used for 2013, growth will gradually accelerate, as demand the purchase of imported equipment, thus leading firms up and overall economic activity picks up to a stronger than expected growth in imports. steam. The baseline scenario is one of a recovery in credit flows, to the economy supporting an Figure 2.1:A pickup in growth in 2013-14 investment led recovery. Government is assumed Growth outlook 2013-15 to maintain a prudent fiscal stance, and hence, 7 seek to consolidate fiscal policy in the outer years 6.5 6.7 Growth rate (Percent) of the forecast. Expenditures will still expand, 6 6 but not like in the pre-election year. Exports are 5.5 5.7 5.9 assumed to grow in line with the strengthening 5 of the economies of Kenya’s trading partners. 4.5 4.4 4.6 4.1 4.4 And imports are dependent on the strength of 4 domestic demand, particularly for capital goods. 3.5 The World Bank forecasts accelerated growth 3 2011 2012 2013 2014 in the second half of 2013, driven by private Pessimistic Baseline Optimistic sector net lending and strong performance in Source: World Bank staff calculations the booming agricultural sector. Kenya’s growth outlook for 2013 closely mirrors projected SSA Table 2.1: Macroeconomic Indicators 2008-2014 2009 2010 2011 2012 2013** 2014** 2015** GDP 2.7 5.8 4.4 4.6 5.7 5.9 5.5 Private Consumption 5.0 7.2 3.0 5.5 2.9 3.1 2.8 Government Consumption 3.8 6.3 5.2 9.3 4.6 3.7 3.0 Gross Fixed Investment 2.8 7.7 12.6 11.5 12.1 15.0 13.2 Exports, GNFS -9.3 17.4 6.6 4.7 5.4 6.4 6.7 Imports, GNFS 2.8 6.1 15.6 12.5 5.8 8.0 7.7 Source: World Bank Staff calculations ** - Forecasts 24 June 2013 | Edition No. 8 The State of Kenya’s Economy In the low case scenario, GDP growth could A combination of accommodative monetary remain at below potential as macroeconomic stanceand rapid credit growth will spur instability induced by the realization of risks in consumption and private investment in 2013. Kenya’s current account deficits (fast depreciating Domestic demand growth may be higher than shilling), as well as overheating from excess projected, supported by strong capital inflows and liquidity (high inflation), both of which combine eased financing conditions. With economic activity to reduce investment activity and consumer and capital inflows accelerating, lenders are spending. Government and export activities are expected to ease credit conditions, further driving assumed to be less affected, however, imports are up aggregate demand. The World Bank expects a also reduced. sharp pick up in private consumption during the second half of 2013, driven by sharp increase in Figure 2.2: Monetary policy space is available to support growth credit and increased gross investment,as investors 25.00 rush to implement plans which have been on hold Monetary policy space - The gap between Actual CBR and simulated CBR indicates the leaverage which the waiting for the new government to be in place. Central Bank has to stimulate the economy 20.00 Domestic demand will continue to power Kenya’s 15.00 GDP growth, with public infrastructure investment playing a leading role. The drag on growth 10.00 Actual CBR movements from net exports will ease, as global economic 5.00 Overall Inflation conditions improve. Inflationary pressures should Simulated CBR which reflects Inflationary Presure and remain in check, and Kenya can expect to receive 0.00 domestic capacity utilisation continued inflows of short term capital into its Feb-10 Apr-10 Jun-10 Aug-10 Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 Dec-11 Feb-12 Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 fixed income securities and equities market. Prices in the equities market have increased significantly Source: World Bank Staff calculations based on CBK data in the first quarter of 2013, and should this continue, it will inspire consumption as a result of With inflation no longer being a significant threat, the increasing wealth effect. In addition, with the the Central Bank’s softer monetary policy stance discovery of oil and gas reserves, Kenya will attract has stimulated aggregate demand and growth. To higher FDI flows to finance exploration in 2013, stimulate economic activity, the Central Bank has and these flows will continue into the medium been easing the monetary policy since the second term. half of 2012, by lowering the CBR to 8.5 percent from 18 percent by May 2013. Commercial banks Macroeconomic policies are expected to remain have started to ease their rates, with the average generally accommodative to support growth. lending trending downwards. The impact of As the supply side constraints in agriculture ease CBK’s action is already evident, as credit growth due to adequate rainfall, the government will has been increasing since November 2012, and try to balance fighting inflation and supporting is expected to accelerate in the second and third growth. Even though inflationary pressure has quarter as of 2013, as borrowers respond to lower moderated with core inflation subdued, inflation lending rates. Within 6 months leading to April is expected to edge upwards as private demand 2013, credit grew by 132 percent with commercial picks up. Inflation could move above the 5 percent banks having lent KES 74 billion compared to 32 medium term target in the second half of 2013, billion in the same period in 2012. Most of the without necessarily triggering a CBR hike, provided credit went to private households (22 percent), medium-term inflation expectations remain well business activity (19 percent), domestic trade (18 anchored. This may help bring down lending percent), and manufacturing (18 percent). rates. With monetary policy space available, the deleterious effects of an easy monetary policy June 2013 | Edition No. 8 25 The State of Kenya’s Economy triggering high inflation in the near term are lending and economic activity propel each other reduced. In addition, fiscal consolidation would forward. In addition, further easing of credit reduce the pressure on the external account and conditions will reaccelerate economic activity. mitigate the impact of the appreciation of the real The provision of interest free loans to youth and exchange rates. Moreover, the shift of spending women, as pledged by the new administration from urban towards rural counties envisaged during the election period, if implemented under the devolution processmay contribute to without careful targeting, could pose problems economic growth, as the multiplier effects of for the monetary authorities. The advantages additional spending are expected to be greater in of credit growth to power economic activity rural areas. should be assessed against the risks of generating inflationary pressures. As figure 2.3 shows, private 2.2 Risks to outlook sector growth and inflation are strongly correlated. K enya’s short-term risk picture improved remarkably at the end of first quarter, following the successful elections and the As highlighted in previous edition of the Kenya Economic Update, the key lesson learned through the 2011 crisis, is the need for policymakers to peaceful transition of power. The main risks to the react fast to anchor market expectations and growth outlook for Kenya stem from: (i) continued prioritize the fight to reign in on inflation, as a high current account deficit; (ii) inflationary risks necessary condition to assure sustained growth. associated with monetary easing to stimulate As such, gradualism in monetary policy easing is a growth and increases in electricity prices; (iii) poor more preferred approach. implementation of the budget affecting service Figure 2.3: A close relationship between Inflation and credit to private sector delivery; (iv) security threats from terrorists; and, As much as food and transport are the drivers of overall inflati on, (iv) fiscal risks associated with the devolution 40 growth in private sector credit is a signi�cant factor process and demands for higher salaries of public 35 officials, could fuel inflation further. 30 Percentage Growth 25 The high current account deficit continues to pose 20 a risk and vulnerability to Kenya’s macroeconomic 15 stability. Kenya’s large and persistent current 10 account deficit of over 10 percent of GDP in the 5 last three years, raises a major concern for Kenya’s 0 Dec-03 Aug-04 Dec-04 Aug-05 Dec-05 Aug-06 Dec-06 Aug-07 Dec-07 Aug-08 Dec-08 Aug-09 Dec-09 Aug-10 Dec-10 Aug-11 Dec-11 Aug-12 Dec-12 Apr-04 Apr-05 Apr-06 Apr-07 Apr-08 Apr-09 Apr-10 Apr-11 Apr-12 Apr-13 sustained economic growth. The short term Private sector credit Overall Infl ation flows which Kenya relies on to finance the deficit Source: World Bank Staff calculations based on CBK and KNBS data could become volatile, triggering a disorderly adjustment. Moreover, the current account deficit is bound to stay high, driven by high capital imports Demands for more public spending pose a fiscal and high investment demand. In addition, the risk. There are several demands to increase public weak and subdued demand for Kenya’s exports in spending. First the demand to hire over 100,000 its traditional European markets will remain a drag additional teachers, despite studies indicating on Kenya’s current account, as euro zone battles a high rate of class absenteeism of up to 36 recession. percent poses a fiscal risk for Kenya. The national teachers union has also called for mass promotion Inflationary risk associated with monetary easing of its members along with hardship allowances, to stimulate growth needs to be taken seriously. which are at variance with notional government The easing of monetary policy will trigger budget allocations. Secondly, demands by elected significant demand for private sector credit, as representatives at national and county levels 26 June 2013 | Edition No. 8 The State of Kenya’s Economy to be paid salaries at rates way above those foster job creation, i.e. reinvigorate both engines recommended by the Salaries and Remunerations of the economy. The best way to achieve this is Commission (SRC) will significantly drive up the to maintain macroeconomic stability, to develop a wage bill. Thirdly, there are still unresolved issues business environment that promotes investment associated with devolution, which have fiscal and job creation, and to increase the stock of implications and need to be sorted out without physical and human capital. threatening the public sector wage bill, including: (i) the integration of local authorities’ staff with Developments in exchange rate suggest that civil servants whose national functions have been Kenya’s real effective exchange rates are far decentralized to the counties; and, (ii) emerging from levels consistent with medium-term demands among public servants for higher wages. fundamentals. Kenya’s real exchange rate has appreciated strongly in the last decade, and this Other risks are also present. Security threats has been accompanied by the deterioration of from Al Shaabab and the Mombasa Republican thecurrent account balance. For any exchange Council (MRC) are hurting the tourism industry rate regime to remain stable and competitive, and investment in the coastal region, and parts of real exchange rates require a supportive policy Nairobi. The activities of these insurgent groups environment, which would include prudent have led to tourist cancelations and might deter macroeconomic policies and a strong financial investors from investing in Mombasa, Kenya’s sector. second largest city. Secondly, the proposed increase of over 100 percent in electricity tariff Kenya’s policy makers have to confront the planned to take effect in 2013, will increase the challenge of managing the surge in short term cost of doing business in Kenya for manufacturing capital flows and the associated vulnerabilities industries. Electricity costs to a sudden reversal of the inflows. in Kenya are already high, In recent years Kenya’s economy has when compared to Kenya’s benefited from large short term flows competitors in this sector. A attracted by the open capital account, huge increase in electricity Since domestic savings one of the most vibrant bond and fixed tariff will hurt business for are low, attracting FDI income securities markets in Africa, those in the export sector. would supplement as well as the underlying strength and domestic savings in potential of the domestic economy. 2.3 Important priorities for financing Kenya’s However, strong capital inflows have the near and medium growth agenda, contributed to the appreciation of the term real exchange rate. T he key challenge for the medium term remains boosting productivity and regaining competitiveness. To maintain high growth Foreign Direct Investment is key to Kenya’s development agenda. Since domestic savings are low, attracting FDI would supplement domestic rates, Kenya needs to continue investing more savings in financing Kenya’s growth agenda. in infrastructure and human capital, improve Kenya should aggressively seek more productivity the business and regulatory environment, and enhancing FDI to diversify its economy and develop diversify exports. Strong import growth, lackluster its private sector, encouraging technology transfer export growth, and an appreciating real effective to sharpen its competitive edge in the external exchange rate, are driving the growing current market. Kenya needs strong judicial institutions to account deficit. The challenge for Kenya is to resolve conflicts, enforce contract disputes, and engineer policies to boost productivity growth and ensure a level playing field for investors. Political June 2013 | Edition No. 8 27 The State of Kenya’s Economy Box 2.1: Higher savings for faster growth The economic literature finds that domestic savings are crucial a component for high and sustainable growth in open developing economies. Economic developments of the past few decades confirm the theoretical findings. The Growth Report (2009), which analyzes the factors behind the 13 most successful economies in the post-World War II period, illustrates the importance of high investment for achieving rapid growth. The common feature of these “success stories� is that they had relatively high saving rates at the beginning and during their “high growth episode�. High savings rates are particularly common in East Asia -the fastest growing region of the world. The average savings rate in East Asia during the 2000s was 30 percent of GDP, compared to the global average of about 19 percent. Sub-Saharan Africa (SSA) is the poorest region of the world and also has the lowest-though rapidly increasing-rate of saving. SSA’s average savings rate has gone from 10 percent of GDP in the 1990s to 14 percent of GDP during the 2000s, and by 2011 it reached 17 percent of GDP. Kenya’s savings rate has not followed the same trend as the rest of SSA. The savings rate has been lingering around 13-14 percent of GDP over the last five years, and is much lower than the average for low income countries (26 percent of GDP). In contrast, neighboring Uganda and Tanzania have already passed the 20 percent mark even though their GNI per capita is less than $550 compared to Kenya’s $820. Kenya has succeeded to attain the same investment rate as SSA (17 percent of GDP) with lower rate of savings, partly owing to higher inflows of foreign savings. Nevertheless, it would be difficult to reach the needed investment rate to meet the Vision 2030’s development goal by relying solely on foreign savings. Uganda and even more so Tanzania have achieved remarkable investment rates, and high savings rates are a big contributor to this success. Increasing Kenya’s savings rate will not by itself lead to the desired 10 percent annual GDP growth rate. Higher foreign investment is necessary in order to bring superior technologies and know-how into the country. More importantly, unless Kenya’s economy realizes progress in productivity –which is currently constrained by numerous factors such as poor infrastructure and weak governance- the desired economic development will not be achieved even with the most ambitious savings efforts. Figure 2.4: Savings and GNI per capita (2011) Figure 2.5: Savings and investment, as at GDP (2011) 35 4000 Low income South Asia 30 3500 Uganda LMIC 25 3000 SSA GNI per capita, in US$ 2500 20 Tanzania Savings rate 2000 SSA LMIC 15 1500 10 1000 South Asia Low income Kenya 5 500 0 Kenya 0 0 5 10 15 20 25 30 35 0 10 20 30 40 Savings rate Investment rate Source: World Bank Source: World Bank Source: World Bank 28 June 2013 | Edition No. 8 The State of Kenya’s Economy stability is also an important factor in attracting Kenya’s medium term plan must address the issues FDI, and in providing longer term investment of economic growth, equity and macroeconomic horizons. stability and sustainability. As the economy has not generated enough modern jobs for the Increasing investment will also need to rely on burgeoning youth, and as poverty levels are still higher domestic savings. While FDI is beneficial at high levels, significant proportion of Kenyans and should be promoted, achieving the investment with limited employability are being squeezed rate needed for sustainable into vulnerable, insecure, low- rapid growth will require higher paying jobs, mostly in the informal propensity to save (see Box 2.1). sector or subsistence agriculture. Savings in Kenya are low compared The situation is even worse among Kenya’s policy to other countries in the region, women, youth, as well as other agenda includes and far from the levels of fast- groups that have one or more economic rebalancing, growing countries (in particular in characteristics that become the strengthening private East Asia). ground for discrimination. Shrinking investment, improving the informal sector requires both tax and spending The ultimate objective of Kenya’s positive and normative actions, policies, and addressing development strategy is to by improving their productivity, rapid demographic make it more inclusive. The and implementing better labor shifts standards. Both policies and new administration promises to make growth more inclusive. This can only be goals on addressing inequality must be part done through reforms to promote economic of the MTP II agenda for the next 5 years. The diversification and job creation, tackling agenda should focus not only on growth, but it infrastructure gaps, and enhancing the human should recognize the importance of growth for capital and productivity of the poor. Again, Kenya’s employment creation, and improved well-being. policy agenda includes economic rebalancing, As such, a broader objective including inclusive strengthening private investment, improving growth and goals and targets on employment may tax and spending policies, and addressing rapid be appropriate (see also UNDP/ILO Report May demographic shifts. Kenya should also undertake 2012). coordinated and collective action to deepen regional trade integration. June 2013 | Edition No. 8 29 30 June 2013 | Edition No. 8 Special Focus: Poverty Special Focus: Poverty 3. Poverty W ith strong economic growth, a peaceful political transition, a new constitution and a rapidly growing and educated labor force, Kenya has growing potential to tackle poverty. In 2005, close to 17 million Kenyans (47 percent of the population) were estimated to be living in poverty. As there has not been another nationally representative household budget survey since 2005 that enables poverty measurement, it is not known exactly how poverty has changed in the past 8 years. However, rapid economic growth is driving poverty reduction across the region and projections using national accounts data suggests that Kenya’s poverty rate is in the range of 34 and 42 percent. Broader measures of welfare point to a Kenya that is increasingly healthy, more educated and more connected, but a large proportion of Kenyans still live without access to clean water, good sanitation facilities and electricity. What can the government do to accelerate poverty reduction? By sustaining growth through sound fiscal and monetary policy; encouraging manufactured exports and improving the business environment so that more productive jobs for low and middle skilled workers are created; supporting smallholder farmers by connecting them to productive assets and markets; strengthening and expanding targeted cash transfer programs, and ensuring basic services are more accessible and responsive to everyone regardless of their location, wealth, ethnicity or gender. Lastly, a routine system of poverty monitoring with household budget surveys as a foundation is needed to understand where, how and why poverty is changing, and to inform Kenya’s policy efforts in the fight against poverty. 3.I Poverty in Kenya years (Figure 3.1). Countries that devote resources A t a time of major social and economic transitions, the conditions for attaining better living standards are increasingly within to tracking social outcomes are in a better position to learn about the impact of their policy choices and make incremental improvements to policies reach for a majority of Kenyans. In the past over time. twenty years, Kenya’s economy has gone from Figure 3.1: Kenya needs to ramp up poverty monitoring shrinking to growing at nearly 5 percent per year; Poverty surveys by region between 1980-2010 (average) jobs, once predominantly in farming, are now 3 4 Sub-Saharan Africa Kenya predominantly in non-farm self-employment and wage work; families are smaller and more likely to South Asia 5 settle in towns and cities; and people have more 3 education and skills than ever before. Behind Middle East & North Africa each of these transitions are each Kenyan’s quest 12 for opportunity, and a desire for a better life for Latin America themselves and their children. 9 Europe and Central Asia Despite these major social shifts, we know little 7 East Asia and Paci�c about how poverty has changed. Efforts to measure poverty and welfare in Kenya have been sporadic Source: World Bank and inconsistent. In the 30 years spanning 1980 to 2010, Kenya conducted four surveys that provided The latest reliable poverty estimates are a basis to measure poverty—an average of one almost a decade old. The 2005 Kenya Integrated survey every 8 years. In Latin America, surveys Household Budget Survey (KIHBS) was the last that enable poverty monitoring are conducted nationally representative survey conducted by once every 3 years, and in East Asia once every 4 the Government of Kenya to measure poverty. 32 June 2013 | Edition No. 8 Special Focus: Poverty Without more frequent surveys, there has been and is concentrated in rural areas. Based on a missed opportunity to understand whether the Kenya’s national poverty line, close to half of the economic gains that have been achieved in the population (close to 17 million Kenyans) was poor past decade, have generated opportunities widely in 2005 and the vast majority of the poor lived in for Kenyans and pathways out of poverty for the rural areas. Poor households are also more likely poor. to depend on income and consumption from crops and livestock, as a source of livelihood (Table 3.1). Poverty and well-being are often understood in terms of income. Today’s most widely used Poor people are more likely to have low education measure of poverty is the number of people living levels and be part of larger families. Primary and on less than 1.25 dollars a day—the extreme poor. secondary school completion rates are the lowest The Millennium Development Goals adopted this amongst the poorest individuals. In 2009, the measure for its target of reducing by half the rate average size of households among the poorest of poverty between 1990 and 2015, and the World 20 percent of households was 5.2 compared, to Bank recently endorsed the goal of reducing the a national average of 4.3, and an average of 3.5 percentage of extreme poor to 3 percent by 2030. among the wealthiest households. Kenya’s own measure of poverty is based on the cost of purchasing a basket of food items which Day to day hardship accompanies the condition provides just enough calories (2,250 kilocalories) of poverty. Among the poorest Kenyans, 99 out to meet daily requirements and an allowance for of 100 live without electricity and without a flush basic non-food amenities (Box 3.1). toilet, 80 out of 100 share a living space with two or more people, and 64 out of 100 do not have Survey data from 2005 indicates that the scale access to an improved source of water. of consumption poverty in Kenya is staggering, Table 3.1 : Looking back - patterns of poverty in 2005 Kenya Overall Nairobi Other Urban Rural Mean 95% CI Mean 95% CI Mean 95% CI Mean 95% CI Fraction of the population 100 -- 7.5 -- 19.6 -- 68.3 -- Population in 2005 35.6 -- 2.7 -- 7.0 -- 24.3 -- Fraction of the population below the 46.7 44.6–48.6 22 14.4 - 29.6 42.5 37.9 - 47.1 49.7 47.6 - 51.9 national poverty line (%)1 Number of poor below the national 16,630 589 2,961 12,094 poverty line (thousands) Fraction of population below $1.25 43.3 41.1 - 45.5 5.8 2.8 - 8.7 14.3 11.3 - 17.2 51.4 49.2 - 53.6 per day poverty line (%)2 Number of poor below $1.25 per 15,419 155 996 12,508 day poverty line (thousands) Poverty headcount by occupational 54.7 52.4 - 56.9 7 -5.8 - 19.7 53.4 41.9 - 64.9 54.9 52.6 - 57.2 sector of household head: Industry 42 37.3 - 46.7 24.6 14 - 35.2 50.8 43.5 - 58 46 40 - 52.1 Services 32.4 29.5 - 35.4 21.6 13.1 - 30.1 39.1 33.7 - 44.4 32.4 28.8 - 36 Share of employment by sector 57.1 54.7 - 59.6 3.3 1 - 5.6 7.5 5.8 - 9.3 69.1 67.1 - 71.1 (%)3: Industry 8.8 7.8 - 9.9 23.4 18.3 - 28.5 17.7 15 - 20.4 6.7 5.7 - 7.7 Services 34.1 32 - 36.1 73.3 68.1 - 78.6 74.8 71.7 - 77.9 24.2 22.5 - 25.9 Total 100 -- 100 -- 100 -- 100 -- Gini coefficient 0.516 -- 0.581 -- 0.410 -- 0.383 -- Source: World Bank analysis of 2005 Kenya Integrated Household Budget Survey June 2013 | Edition No. 8 33 Special Focus: Poverty Box 3.1: Kenya’s poverty line In 2005, the cost of basic food and non-food needs per month for one adult was established at KES 1,562 for rural areas and KES 2,913 for urban areas. Throughout this report, the poverty rate or headcount refers to people living in households with per adult equivalent expenditures below these amounts. Adjusting for increases in prices since 2005 using the Consumer Price Index (CPI), the approximate value of the rural poverty line in 2012 was KES 2,900 per month for rural areas and KES 5,400 per month for urban areas. Kenya’s poverty lines expressed in 2005 international dollars – the unit of measure used by the global 1.25 “dollar per day� measure of poverty—were approximately 1.57 dollars per day per person for rural areas, and 2.9 dollars per day per person for urban areas. Expressed in Kenyan shillings, the 1.25 poverty line in 2005 was approximately KES 1,246 per day. Using this benchmark, Kenya’s 1.25 dollar a day poverty rate in 2005 was 43.3 percent overall. Source: World Bank Poverty rates are highest in the arid and semi- Low income or material deprivation alone does arid regions in the north and north east. Areas not constitute the full experience of poverty. with very little annual rainfall, and thus, low Global consultations with the poor reveal the agricultural potential have acute poverty (Figure overwhelming anxiety, grief, hunger, stress and 3.2). These regions have also been historically low self-confidence associated with poverty. These neglected, reflecting Kenya’s unbalanced emotions are linked to lack of security, power, geographical development. In 2005, poverty rates poor health, discrimination and unstable work. in arid regions (78 percent) were nearly double In recognizing that income is not the only thing the poverty rates in medium and high potential people care about, broader measures of poverty agricultural areas (with poverty rates averaging 41 that attempt to capture a more complete picture of percent). well-being have emerged. Due to patterns of population density, the largest Broad indices of well-being indicate improving numbers of poor are concentrated in areas where living conditions for Kenyans. The Multi land is most fertile. Medium to high potential Dimensional Poverty Index (MPI) developed by the agricultural areas only make up 20 percent of all Oxford Poverty and Human Development Initiative, land, yet are home to 80 percent of the population. combines ten deprivations (each with a specific As a result, the largest numbers of poor are found weight) in the categories of health, education and around the shores of Lake Victoria in the west, the living standards into one overall index. According to central highlands around Nairobi and east of Mt. this measure, a household is poor if it experiences Kenya and the coast near Mombasa (Figure 3.3). at least a third of all possible weighted deprivations. With rapid and concentrated population growth Using Demographic and Health Survey (DHS) data, in these areas, the pressure over productive land the percent of poor people according to the MPI resources will continue to grow. declined from 60.1 to 51.2 percent between 2003 and 2008 (Alkire & Roche, 2013). Another measure, Inequality in Kenya is high, especially among the Human Development Index (HDI) developed households in urban areas. In 2005, the average by the United Nations Development Programme per adult equivalent expenditures among the combines life expectancy, mean years of schooling bottom ten percent of households was KES 466 and Gross National Income (GNI) per capita in a and KES 1,110 in rural and urban areas—more than single index. In 2012 Kenya’s HDI was 0.519, ranking ten times smaller than the average expenditures it 145 out of 187 countries with comparable data. among the top ten percent of households (KES Since 1990 (when Kenya’s HDI was at 0.463) the 5,741 and KES 22,823 in rural and urban areas, HDI has improved at a rate of 0.5 percent annually respectively). (UNDP, 2013). 34 June 2013 | Edition No. 8 Special Focus: Poverty Figure 3.2: The North and north-eastern, arid and semi-arid regions are the poorest regions in Kenya Poverty estimates at the sub-location level, 2009 Source: World Bank June 2013 | Edition No. 8 35 Special Focus: Poverty Figure 3.3: The poor are concentrated where land is most fertile Number of poor per square kilometer by sub-location, 2009 Source: World Bank 36 June 2013 | Edition No. 8 Special Focus: Poverty Kenyans primarily equate poverty with an The proportion of Kenyans describing their living inability to meet basic human needs and a lack conditions as very bad or fairly bad doubled of cash income. The 2003 Afrobarometer asked from 36 to 72 percent between 2003 and 2011. Kenyans what they think about poverty. “Lack of While responses to an opinion-poll should not food� and “Lack of money� were the most common be interpreted as evidence that welfare in Kenya responses along with lack of shelter and clothing. is worsening, it is worth noting that Kenyans Poverty was also frequently associated with not increasingly think of and perceive their living having productive assets such as land or livestock; conditions in more negative terms. Understanding not having key opportunities such as an education what is driving these perceptions could yield or a job; and being physically handicapped (Figure important insights about what Kenyans value the 3.4). most, when thinking of their own living standards. Among a range of poverty-related experiences, Many Kenyans expect the government to Kenyans most frequently report having lived reduce the gap between the rich and the poor, without earning cash income. In 2011, almost and to ensure that people have access to basic three in four adult Kenyans said they went without necessities. Inequality in Kenya is fairly high, and a cash income, “many times�, “several times� or about one in five Kenyans think that an essential “always� during the year. The next most common part of democracy involves a government that experiences of deprivation (reported by one in works to reduce inequality in society, and provide three adult Kenyans) were not having enough food support to help families meet basic food, shelter to eat, going without medical care or treatment, and clothing needs. and not having enough clean water for the home. Figure 3.4: Kenyans associate poverty with lack of food and money Frequency of words in response to “What do you associate with poverty?� Source: World Bank June 2013 | Edition No. 8 37 Special Focus: Poverty Kenya can do more with the resources it has to and South Africa reduced poverty at over 2 percent reduce hardship and improve well-being. Among per year over a period of between 5 and 7 years in countries with comparable GDP per capita, the 2000s (Figure 3.4). In Uganda, poverty rates Kenya is an average performer with respect to fell from 39 percent to 25 percent between 2002 available measures of well-being, and is far from and 2009. Rwanda, another one of Kenya’s close the frontier of what is possible. For example, neighbors, also made major gains against poverty; while Nepal and Rwanda have lower income per the drivers of this success are explored in more capita compared to Kenya, their newborns are 30 detail in Box 3.2. percent more likely to survive to the age of five. In Ghana, another country with a similar income In Kenya, how poverty has changed is not level, the percentage of people experiencing clear because of the lack of regular household multiple deprivations is 30 percent lower than in budget surveys. It is not known with certainty Kenya (Figure 3.5). how poverty has changed since 2005, and until a new household budget survey is conducted, the 3.2 How has income-poverty changed? poverty level will not be known. P overty rates in sub-Saharan Africa are falling. Using all available data, the World Bank estimates that the percentage of people living This section presents results from two methods to estimate the likely trajectory of income poverty in on less than $1.25 per day in sub-Saharan Africa the past 20 years. It should be noted up front that fell from 56.5 to 48.5 percent between 1990 and these methods, used by the World Bank and other 2010, at a rate of about 0.8 percent per year. The institutions in settings where survey estimates majority of this decline occurred during the 2000s, of poverty are not available, rely on strong fueled by GDP growth which averaged 5 percent assumptions and produce estimates that are used per year. Given population growth, poverty rates only to get a first order approximation of changes, have not declined fast enough to reduce the not specific poverty levels. The first approach number of poor in the region, which increased models the trajectory of consumption per capita, from 290 to 413 million between 1990 and 2010 both forwards and backwards in time from 2005 (Figure 3.4). by applying observed growth rates in GDP per capita from national accounts data to household Experience from the region suggests that poverty consumption from KIHBS data. The second reduction can occur at a very fast pace. Among approach uses information on characteristics of countries with available data, Uganda, Rwanda households to predict consumption and poverty Figure 3.5: Kenya’s welfare indicators in an international perspective Purchasing Power Parity GDP per capita vs. Under-five mortality and Multidimensional Poverty Latin America & Caribbean Global relationship between Multidimenstional Poverty (percent of pop.) 190 Europe & Central Asia Sierra Leone East Asia & Paci�c Mali Ethiopia GDP per capita and Mali South Asia Guinea Burkina Faso multidimensional poverty Chad Middle East & North Africa Guinea-Bissau Sub-Saharan Africa 80 Sierra Leone Senegal Kenya Under-�ve mortality rate 155 Global relationship between Rwanda Uganda Benin Burkina Faso GDP per capita & Under-5 mortality Madagascar Timor-Leste Tanzania Chad Zambia Cote d'Ivoire Mauritania Gambia, The Guinea Cameroon Nigeria 60 Haiti Bangladesh Nigeria 120 Togo Cote d'Ivoire Mauritania Togo Cameroon India Yemen, Rep. Kenya Pakistan Benin Lao PDR Cambodia Mozambique Afghanistan Gambia, The Nepal Uganda Sao Tome and Principe Lesotho Sudan 40 85 Comoros Ethiopia Zambia Ghana Yemen, Rep. Lesotho Sao Tome and Principe Kenya Pakistan Ghana Haiti Djibouti Tanzania Senegal Tajikistan Madagascar Papua New Guinea Rwanda Timor-Leste 50 Nepal Bangladesh Kiribati Uzbekistan Cambodia Lao PDR Micronesia, Fed. Sts. 20 Tajikistan Mongolia Iraq China Kyrgyz Republic Guyana Solomon Islands Vietnam Kyrgyz Republic Vietnam Uzbekistan Moldova Moldova 15 0 800 1200 1600 2000 2400 2800 3200 800 1200 1600 2000 2400 2800 3200 GDP per capita (Log Scale) GDP per capita (Log Scale) Source: World Bank 38 June 2013 | Edition No. 8 Special Focus: Poverty Box 3.2. A small country with big declines in poverty The role agricultural production and off-farm income in poverty reduction in Rwanda Between 2001 and 2011 Rwanda’s poverty head count fell from 59 to 45 percent, a major success for the country in the fight against poverty. During this time, Rwanda posted average annual GDP growth of over 8 percent, translating into real improvements for households which now enjoy real consumption levels that are thirty percent higher than they were in 2001. More crop per acre, more cash per crop More than 70 percent of Rwandans depend on agriculture for income, and it is the single most important source of income for the poor. It is no surprise then that increased agricultural production was a major driver of consumption gains and poverty reduction. In the past decade, agricultural production doubled at the household level, as did the share of households selling surplus harvests on the market. Behind these developments were increased investments in agricultural inputs, land consolidation and infrastructure. Specific public programs such as the Crop Intensification Program (CIP) and the Land Husbandry, Water Harvesting and Hillside (LWH) irrigation supported production gains, as did dramatically increased fertilizer use made possible through Government subsidized fertilizer imports. Don’t put all of your eggs in one basket Rwandan households have increasingly taken up non-farm activities such as wage jobs and small businesses in addition to farming. In 2001 less than 30 percent of households had a non-farming activity, by 2011, the share of households with livelihood activities off the farm shot up to 70 percent. Interestingly, families did not abandon farming; they added income activities both as primary and secondary occupations. This has helped households to reduce the risk of bad weather that accompanies rain fed farming and provided a more regular source of cash income which boosted consumption. Source: Rwanda Economic Update 2013(World Bank, 2013) in surveys that do not measure consumption is distribution-neutral on average (Ravallion, directly. Annex 1 describes the methodology for 2004). both of these approaches in more detail. Among countries in sub-Saharan Africa with Assuming growth rates in GDP per worker closely available data, inequality fell by 0.5 percent per follow the growth of household consumption, it year on average between 1990 and 2010. The is likely that poverty has declined in Kenya since fastest rise in inequality (measured by the Gini 2005, but the degree of decline depends on coefficient described in Box 3.3) in the region whether inequality has risen or fallen. Between during this period was over five percent per year 2005 and 2011, per-worker income (adjusted for in the Seychelles, and the fastest decline was 4.2 inflation) increased at approximately 2.2 percent percent per year in Malawi (the Gini fell from 50 to per year on average. In a setting where this growth 38 between 1998 and 2004). translates to increased consumption of 2.2 percent per year for all income levels, inequality would Given that Kenya’s economic growth is driven by not change and poverty would fall. However, if domestic consumption and services, rather than growth disproportionately benefitted the already extractive industries; it is likely that the benefits wealthy or middle class, income inequality would from growth have been spread broadly across increase and poverty would fall at a slower rate income groups. In contrast to countries where vast (and possibly even increase). oil or mineral endowments have driven growth such as in Nigeria, Equatorial Guinea or Angola, Among growing economies, inequality tends to Kenya’s economic growth in the past ten years has fall as often as it rises. A study of the relationship not relied on commodity exports. The historical between growth and inequality over time within experience with commodity driven growth is that countries, found that historically, economic growth it benefits the few at the expense of the many. June 2013 | Edition No. 8 39 Special Focus: Poverty Figure 3.6: A decade of poverty reduction in Kenya’s pattern of growth gives more plausibility Sub-Saharan Africa US$1.25 dollar a day poverty in the 2000s to scenarios of moderate, rather than extreme changes in inequality. However, given entrenched 70 inequality between population groups defined by Madagascar ethnicity and geography, the slow overall rate of increase in agricultural productivity and the urban nature of much of Kenya’s growth (Nairobi and 65 Mombasa alone account for about 40 percent of the country’s wage earnings) it is difficult to say Swaziland Nigeria whether inequality has increased or fallen. 60 In scenarios of moderately changing inequality Togo (from a decline in the Gini coefficient of 1 percent per year, to an increase of 1 percent per year), Guinea-Bissau national accounts based projections suggest 55 Population living on less than US$1.25 per day Guinea that poverty declined from 47 percent in 2005 Mozambique to somewhere in the range of 34 and 42 percent in 2011. In rural areas, this scenario predicts Malawi that poverty fell from 50 to between 38 and 46 50 percent, and in urban areas from 34 to between 22 and 28 percent (Figure 3.7). (Annex 2 illustrates Senegal the impact of inequality on the distribution of Burkina Faso Congo, Rep. consumption). 45 Rwanda Kenya Mali While growth is a necessary condition for Cote d'Ivoire Mauritania poverty reduction, growth alone may not lower poverty. Even though Kenya sustained growth in 40 Cameroon real income per capita between 2005 and 2011, a 2 percent per year increase in income inequality over this period would have erased the poverty 35 reduction that would have otherwise accompanied that growth (Figure 3.8). These simulations show that reducing inequality 30 accelerates the poverty-reducing effect of Ethiopia economic growth. For each percentage point Namibia Ghana that inequality falls, the poverty rate falls by an additional 1.7 percentage points for Kenya’s 25 Annualized observed trajectory of income growth. percent change: Uganda > 0.5 % 0 to 0.5 % South Africa Data on the characteristics of households such as 0 to -1 % -1 to -2 % the quality of housing, the education of parents 20 -3 to -4 % and the size of the family provides an indirect way to measure income poverty. As many nationally 1996 2000 2004 2008 2012 representative surveys collect in depth data on Year household characteristics, this information can Source: World Bank 40 June 2013 | Edition No. 8 Special Focus: Poverty Box 3.3: Know your Gini! Kenya’s inequality and how it is measured One measure of inequality is the Gini coefficient. The Figure 3.7: The Gini-coefficient in Kenya and the region Gini gauges how far the distribution of income in a population is from a scenario where all income is shared equally. In a perfectly equal society each household has the same income so that both the “bottom� and “top� 38 ten percent of the population both earn 10 percent of all income. In this imaginary society, the Gini is equal to 46 Mozambique-2008 Tanzania-2007 44 Uganda-2009 0. In a very unequal society, the “bottom� ten percent of households earn a much smaller share of all income 49 Nigeria-2010 47 Kenya- 2005 39 Malawi-2004 relative to the “top� ten percent of households. The maximum possible Gini for a country is 100 which would 30 49 be a case where one person owns very close to 100 Ethiopia-2005 Rwanda-2000 percent of all income. In 2005, Kenya’s Gini coefficient was 47—markedly higher than its neighbors to the north and south (Ethiopia and Tanzania) but near the average Source: World Bank of 45 for the region as a whole (Figure 3.7). Source: Rwanda Economic Update 2013 (World Bank, 2013) be used to estimate consumption, even if it is not percent in 1989, to 13 percent in 2009, for other measured directly in the survey. This is achieved urban areas from 44 to 37 percent, and for rural by estimating a consumption model using a survey areas from 56 to 47.6 percent. Figure 3.8 displays that does measure consumption, and applying the poverty estimate obtained from each available the model to surveys or census data without survey as well as the average rate of change consumption data (Table 3.2) implied by these estimates. A consumption model estimated households in 3.3 How have broader measures of welfare Nairobi, other urban and rural areas, and applied changed? to seven surveys spanning 20 years suggests that overall poverty declined by just over 1 percent per year between 1989 and 2009, with the most rapid poverty reduction occurring in Nairobi. This T he availability of census data and other surveys provide an opportunity to assess how non-income measures of welfare have changed modeling technique predicts that poverty rates fell in Kenya. Since 1989,Censuses have been from 53 percent in 1989, to just over 42 percent in conducted every 10 years, and the Demographic 2009. For Nairobi, predicted poverty fell from 30 and Health Surveys every 5 years. Combined, they Table 3.2: Data sources with information on household welfare Data Source Survey Year Sampled Households Household members Census 2009 868,160 3,793,282 DHS 2008 9,057 38,515 KIHBS 2005 13,212 66,725 DHS 2003 8,561 37,612 Census 1999 314,976 1,394,965 DHS 1998 8,380 37,705 DHS 1993 7,950 38,865 Census 1989 217,632 1,066,869 Source: KIHBS = Kenya Integrated Household Budget Survey / DHS = Demographic and Health Survey / WMS = Welfare Monitoring Survey June 2013 | Edition No. 8 41 Special Focus: Poverty Figure 3.8: Did strong growth drive poverty rates down? conditions and an expanded set of consumption Depends how you think inequality has changed National accounts based projections of headcount poverty opportunities. At the same time, a large fraction of in Kenya, 1990- 2011 the population continues to live with sub-standard All Kenya access to water, sanitation and energy, and for 60 many, circumstances given by the sheer luck of one’s birth, such as ethnicity, the wealth of the Poverty Headcount (percent) 49 49 50 47 family and area of residence play an outsize role 46 42 in determining access to basic opportunities (Box 40 Inequality Scenarios 38 34 3.5). Long term change in gini: +2% 31 30 +1% 0% The past decade has been a major success for -1% 20 -2% children’s health in Kenya. While under-five 2005 KIHBS poverty rate 10 mortality rose in the early 1990s to a high of 116 1990 93 96 99 02 05 08 2011 deaths per 1000 live births in 1999, in the 2000s under-five mortality fell by over 4 percent per year Rural to reach 76 by the end of the decade, this was 65 one of the fastest rates of decline in the region. A Poverty Headcount (percent) study of the drivers of infant mortality credits the 55 52 52 50 50 scale-up of insecticide treated bed nets as a major 45 46 contributor to this decline (Demombynes, 2012). 42 38 35 34 Stagnant nutritional outcomes for children, however, point to the need for renewed focus on 25 improving food security and behaviors around 15 child care and feeding. Between 2003 and 2008, 1990 93 96 99 02 05 08 2011 the percentage of children who were stunted an 55 Urban underweight remained at 35 and 16 percent, respectively. 45 Poverty Headcount (percent) 39 36 34 Educational attainment in Kenya has steadily 35 32 increased. Among new entrants to the labor market 28 (defined as individuals between the ages of 25 and 25 25 22 35) the percentage without any formal education 15 18 fell from above 20 percent in 1989, to less than 15 percent in 2009. Overall the average number 5 of years of formal education among 25 to 35 year 1990 93 96 99 02 05 08 2011 olds has increased from 6 to 8 years between 1989 and 2009. The education gap between women and Source: World Bank men fell from 2.5 years in 1989 (7.3 years for mean capture data on educational attainment, child and and 4.9 years for women) to 0.7 years in 2009 (8.5 maternal health, housing conditions, ownership years for mean and 7.8 years for women). of consumer durables and access to electricity among others. Inequality in educational attainment between heads of households has declined significantly. Assessment of a range of indicators points to a Between 1989 and 2009, the Gini coefficient for Kenya that is, on average, increasingly healthy educational attainment of the household head and more educated, enjoying better living measured in years, fell from 0.54 to 0.38 at a rate of 42 June 2013 | Edition No. 8 Special Focus: Poverty Figure 3.9: More evidence that poverty has declined Consumption model predictions of poverty in Kenya, 1990- 2011 100 Kenya 100 Other Urban Annual change: -1.17 % Poverty Headcount (percent) Poverty Headcount (percent) 80 80 Annual change: -0.88 % 60 60 40 40 20 20 Census DHS KIHBS Linear Prediction 0 0 1989 1993 1997 2001 2005 2009 1989 1993 1997 2001 2005 2009 100 Nairobi 100 Rural Annual change: -4.15 % Annual change: -0.82 % 80 80 Poverty Headcount (percent) Poverty Headcount (percent) 60 60 40 40 20 20 0 0 1989 1993 1997 2001 2005 2009 1989 1993 1997 2001 2005 2009 Source: World Bank analysis of Kenya microdata Notes: 95 percent confidence intervals are shown as vertical lines. 2 percent per year. This trend suggests the success A mobile revolution swept Kenya this past of government efforts to expand the coverage of decade, and is creating a platform for delivering basic education. The percentage of household a host of services to the masses. Between 2005 heads without any formal education declined by and 2009, mobile phone ownership increased 3.6 percent per year between 1989 and 2009, and by over 30 percent per year on average, both in at the high end, secondary and post-secondary urban and rural areas. In 2009, almost two thirds education is increasingly common: the share of of all households owned a mobile phone and it household heads with post-secondary education has surely reached a wider share of the population increased by 11 percent per year between 1989 since then, as prices continue to decline. According and 2009 (Figure 3.9). to Afrobarometer data from 2011, over 80 percent of adults owned their own phones, with an additional 10 percent using a phone owned by Early signs of an emerging urban-based middle someone else in the household. class are suggested by the increasing rates of ownership of “middle tier� consumer durables Mobile money has become a fixture of the lives and housing quality indicators. Between 1989 and of Kenyans, including the poor. Launched in 2009, ownership of televisions grew by 9 percent 2007, M-Pesa—the globally known mobile money per year (1 in 3 households owned a TV in 2009), platform has extended a basic form of financial and refrigerators by 4 percent per year. The share access to the population, including a way to of households with high quality materials for the transfer money, make payments and store money roof and floor increased by over 2 percent per safely. Recently, a mobile savings product called year in Nairobi, and access to electricity by about M-Kesho was launched which provides interest 3 percent per year in urban areas. earning accounts on small deposits. June 2013 | Edition No. 8 43 Special Focus: Poverty Figure 3.10: The evolution of household characteristics For the poor, mobile money represents a more Consumer Durables efficient way to receive financial support and Ownership of consumer durables 100 Asset: Annual Growth (%) to save. 10 percent of individuals in the poorest 80 Television: +9.1* Mobile Phone: +31.7* wealth quintile are registered with M-Pesa and Computer: +32* tend to receive more money in remittances than 60 Radio: +1.8* they spend. Six percent of individuals in the Fridge: +3.9* Vehicle: .5 poorest quintile also report saving on M-Pesa, and 40 Motorbike: 8.9 while it does not offer interest, it provides a secure Bike: +1.4* 20 storage mechanism and a potential commitment device to encourage saving. Evidence also 0 suggests that registration to M-Pesa increases the 89 93 97 01 05 09 likelihood of savings by 32 percent (Demombynes Water & Thegeya, 2012). 100 Source of Water Asset: Annual Growth (%) 80 Piped or Public tap: -.1 Owned by one in four households in 2009, Well: +2* Other (Surface Water, Spring, Lake): -1.1* bicycles remain the most common form of 60 private transportation. In Nairobi, ownership of cars and trucks are more common than bikes, [% of households] 40 but ownership of a car or truck has not grown 20 over time, remaining at about 15 percent of households. Households in rural areas increasingly 0 owned bicycles. Ownership of motorbikes has 89 93 97 01 05 09 grown at over 7 percent per year in other urban Sanitation and rural areas albeit from a low base (motorbike 100 ownership was between 3 percent in other urban 80 areas and 2 percent in rural areas in 2009). Waste facility 60 Asset: Annual Growth (%) Flush toilet: +1.2* Access to improved water is evident in the [% of households] 40 Pit Latrine: +.3* modest rate of decline of reliance on surface Other (Bucket, No Facility): -2* water and other unprotected sources of water 20 towards wells. However the overall figures mask 0 the dynamic of declining access to piped or public tap water in the second tier urban areas outside 89 93 97 01 05 09 Nairobi, where household access to piped water Housing/ Energy 100 fell by 2.3 percentage points per year between Housing Infrastructure Asset: Annual Growth (%) 1989 and 2009, and there is even some evidence 80 Roof: Tiles or concrete: +2.5* Floor: Wood or cement: +2.2* of increasing rates of reliance on unprotected Access to Electricity: +4.6* water in urban areas outside of the capital (Figure 60 A1 in Annex 3). [% of households] 40 The fact that top tier water and sanitation 20 indicators (such as access to piped water and a 0 flush toilet) have declined in urban areas outside 89 93 97 01 05 09 of Nairobi, suggests that smaller towns and cities Educational Attainment have not been able to keep up with the rapid 100 Education of head of household rate of urbanization. Between 1989 and 2009, the Source: World Asset: Bank Annual Growth (%) No formal education: -3.6* urban population outside Nairobi grew from 2.2 Some primary: .3 80 Primary complete: 1.3 44 June 2013 | Edition No. 8 Some Secondary: -3 60 Secondary complete: 6.8 s] Special Focus: Poverty Box 3.4: Mais Iguais (“More Equal� in Portuguese) How did one of the most unequal countries in the world become more equal? Inequality has been a persistent feature of Latin America, but beginning in the 2000s, inequality unambiguously declined across the region from an average Gini coefficient of 0.53 in the late 1990s to 0.497 in 2010. The reduction in inequality accounted for 50 percent of the observed decline in poverty during this period. Brazil is widely associated with inequality and it has topped the charts for having at times that highest rate of inequality in the world. But things started to change in the early 2000s: between 1998 and 2009, the Gini coefficient declined by 5.4 percentage points per year reaching 0.537 in 2009. The rate of income growth among the bottom 10 percent of earners (7 percent per year) outpaced the income growth of the top 10 percent of earners (1 percent per year) by a factor of seven. More equal wage earnings across skill levels Researchers found that real increases in labor income per working adult and a moderate decline in its inequality accounted for about half of the decline in overall income inequality. The reduction in inequality in labor income was driven by a narrowing of the gap between the earnings of skilled versus low or unskilled workers. Policies that expanded access to basic education broadly increased the pool of basic skills in the labor market. With a higher supply of workers with basic skills and relatively fewer unskilled or low skill workers the premium on skills declined, making wage earnings more equal across the board. The decline in labor earnings inequality was also due to falling wage differences between similar workers in large versus small cities, urban versus rural areas and between primary versus other sectors. More progressive government transfers The contribution of government transfers also played a big role in equalizing incomes. Since 2001, government programs – especially cash transfer programs – worked to broaden coverage of participants. BolsaFamilia- Brazil’s conditional cash transfer program – increased coverage rapidly reaching 17 percent of households in 2007 from 7 percent in 2001. The equalizing effects of both targeted cash transfer programs and contributory social security had about as large an effect as more equal labor incomes in reducing overall income inequality. Source: Pez-calva, and Ortiz-Juarez, 2012 to 8.9 million people at a rate of 7.3 percent per of water, compared to less poor households in year. By comparison, over this period, Nairobi’s western Kenya because rainfall, natural rivers, population grew by an average of 4.5 percent per streams and lakes are not as common a feature of year from about 1.2 to 3.1 million, while the rural the region’s ecology. population grew by an average of 1.9 percent per year from 17.9 to 26.2 million. The share of households without any waste infrastructure (except possibly a bucket) declined Key indicators of hardship have declined over from 21 percent in 1989 to 13.8 percent in time across Kenya, but the number of households 2009. These improvements were widespread facing day to day hardship remains very large geographically, with the fastest reduction in (Figure 3.10). Between 1989 and 2009, the share sanitation hardship occurring in Nyamira, Bomet of households without any type of infrastructure and Makueni counties, where the share of that enables access to water fell from 50 percent households without any waste facility fell by over to 38 percent. These gains were distributed across 70 percent. Counties where sanitation hardship most geographic regions, with the exception of increased over this period were Homa Bay and two counties: Kitui and Wajir where water-related Vihiga in the western part of Kenya and Lamu on hardship increased from 32.4 to 48 percent in the coast. Kitui and from 10.6 to 14.3 percent in Wajir. Even though households in the more remotely While household access to electricity increased in populated north and northeast are poorer, they Kenya between 1999 and 1989, the improvements are more likely to have access to wells as a source were more concentrated in the counties around June 2013 | Edition No. 8 45 Special Focus: Poverty Figure 3.11: The geographic distribution of hardship in Kenya The percentage of households with sub-standard access to Water, Sanitation and Energy, by county, 1999 and 2009 Sub-standard sanitation:The percentage of households withouta flush toilet or pit latrine of any kind 1999 2009 Percent change between 1999 and 2009 Percent (%) Percent (%) Percent (%) [−100,−50) [0,25) [0,25) [−50,−2.5) [25,50) [25,50) [−2.5,2.5) [50,75) [50,75) [2.5,50) [75,100] [75,100] [50,100] Sub-standard energy:The percentage of households without access to electricity 1999 2009 Percent change between 1999 and 2009 Percent (%) Percent (%) Percent (%) [−100,−50) [0,25) [0,25) [−50,−2.5) [25,50) [25,50) [−2.5,2.5) [50,75) [50,75) [2.5,50) [75,100] [75,100] [50,100] Sub-standard water: households without access to piped water, public tap or a well of any kind 1999 2009 Percent change between 1999 and 2009 Percent (%) Percent (%) Percent (%) [−100,−50) [0,25) [0,25) [−50,−2.5) [25,50) [25,50) [−2.5,2.5) [50,75) [50,75) [2.5,50) [75,100] [75,100] [50,100] Source: World Bank 46 June 2013 | Edition No. 8 Special Focus: Poverty Box 3.5: Give them a chance How opportunities for children depend on their circumstances An important dimension of inequality relates to opportunities that are available to children, where “opportunities� are defined in terms of access to education, health and household infrastructure facilities like water and sanitation. The Human Opportunity Index (HOI) is a measure that quantifies the extent to which existing opportunities for children in the country are equitably distributed across children by “circumstances� into which children are born. These circumstances include gender, economic status, geographic location and household characteristics. There is a growing consensus that equality of opportunity is desirable in that it levels the playing field so that everybody has the potential to achieve the outcomes they choose.There are two reasons why equality of opportunity is relevant for policymakers. The first is that people view as unfair opportunities that are accessed by circumstances rather than effort. The second reason is that inequality can be economically inefficient. In school but without safe water Children in Kenya have high equality of opportunities in education, but low equality of opportunities in health and household services (including adequate water, flooring, sanitation and electricity). Kenya’s HOI for school attendance is amongst the highest in Sub-Saharan Africa, but HOI levels for household services- such as water and sanitation - which are amongst the lowest in the sub-Saharan Africa. Geographic location of households, family wealth and ethnicity shape life chances Wealth, ethnicity and location of residence are circumstances that drive inequality of opportunities for children. Household wealth and ethnicity are the most important circumstances influencing differential access to opportunities in education and health; the geographic location of the households and household wealth are the most important circumstances contributing to inequality of household services such as electricity and water. Source: World Bank Nairobi. After Nairobi (where about one in four The government can alleviate poverty by focusing households do not have access to electricity), public spending and reform efforts on sectors that the greatest improvement in energy-access was increase the human capital of the poor, and that in Kiambu, Mombasa, Kajiado and Nakuru. One allow the poor to access information and markets. in three counties, mostly in the north, north east Making access to basic, but effective health and and far west of the country, did not experience education services easy, affordable and without a meaningful change in electricity access The risks, builds human capital and enables people to hardship associated with not having electricity in seize more productive economic opportunities – the home is still very widespread, over 75 percent whether it is through diversification of employment of households in 40 out of 47 counties in 2009 did into other industries or migration to areas where opportunities are available. Similarly, investing not have any access to electricity. in information networks and infrastructure that connects high density rural areas to Kenya’s urban 3.4 Making public spending work for the poor economic hubs will enable larger numbers of the poor to access markets for their products and W ith the implementation of Kenya’s new constitution, the next decade provides a tremendous opportunity for Kenya to use its labor and thereby increases income opportunities. Targeted interventions that improve productivity public resources to reduce poverty. Kenya’s gains in agriculture will help to reduce poverty. new constitution represents an opportunity for The agriculture sector employs the largest share policy makers and the citizens who elect them of poor households in Kenya, and therefore into office, to restructure and pressure public investments that can improve the productivity agencies to deliver services more efficiently and of smallholder farms, such as fertilizer, improved effectively. This section focuses on the sectors seed varieties, access to markets and introduction where government action can have the largest of higher value added activities, carry great impact on poverty reduction. potential to reduce poverty. June 2013 | Edition No. 8 47 Special Focus: Poverty Better governance and accountability in Education institutions that deliver public services is As of 2011, Kenya’s public expenditure on crucial to ensure that services reach the poor. education was 20 percent of total government As exemplified by high teacher absenteeism in expenditure, and 7.2 percent of GDP. Education Kenya’s schools (Figure 3.14), current governance expenditure increased by 42 percent in real and accountability structures are inadequate and terms between 2004 and 2011 (Government of undermine the effectiveness of public spending. Kenya, 2013). Comparatively, the average public Policies that maximize incentives and effort within expenditure on education in sub-Saharan Africa public service delivery agencies will translate into was much lower—at 3.8 percent of GDP. Of the benefits for poor households. However, public total, the largest share of public spending goes accountability also requires informed and engaged to teachers’ salaries: primary school teachers citizens who can hold leaders and bureaucracies alone account for 57 percent of total government responsible for failures, so transparency and spending on education. openness are also crucial. The poorest Kenyans report having most While there has been considerable improvement difficulties in obtaining key public services, in the level of resources in public schools, learning especially documents and permits, household outcomes remain low, especially among the services such as electricity and help from the poorest students. Results from a recent effort to police when needed (Figure 3.11). Problems track service delivery indicators in Kenya revealed with placing a child in primary school are much that Kenya is doing well relative to other countries less frequently reported, which likely reflects the in the region, in providing key education inputs: benefits of the Government’s efforts to expand in 2012, on average, there were 31 students per access through the free primary education classroom, 33 students per teacher and 3 students program. Large numbers of Kenyans also report per textbook. However, less than one in three having to pay bribes to obtain public services public school students in standard 4 could read (Figure 3.12). Compared to Tanzania and Uganda, a standard 3 level short story. Testing done by Kenya has the highest rate of bribe payments to Uwezo found that students from poor households obtain household services, documents or permits, scored between 20 and 40 percent lower on basic and avoiding problems with the police. Bribe literacy and numeracy exams, than students from payments to obtain medical treatment are also non-poor households. While resources in schools common. have improved, the poorest Kenyans perceive that Figure 3.12: Getting services can be difficult, especially for the poorest Figure 3.13: Nilihonga (“I paid a bribe� in Swahili) Percent of Kenyans reporting difficulty obtaining public Percent of citizens reporting having to pay a bribe to obtain services by wealth quintile services, Kenya, Tanzania and Uganda Avoid problems with 12% 32% 22% Medical Treatment 57% police 33% 19% 14% School Placement Medical Treatment 28% 27% 27% 69% 6% Help from Police 80% Household Services 8% 15% 51% 6% Household Services 76% School Placement 11% 10% 73% 9% Document/Permit 86% Document or Permit 19% 29% Wealthiest Quintile Poorest Quintile Uganda Tanzania Kenya Source: 2008 Afrobarometer Source: 2005 Afrobarometer 48 June 2013 | Edition No. 8 Special Focus: Poverty problems with public schools most commonly have schools, and for greater access to information at a to do with inadequate facilities, overcrowding, lower cost. The Ministry of Education has already lack of textbooks and teacher absenteeism (Figure made effective use of the telecommunications 3.13). infrastructure, by disseminating national exam test scores through mobile phone networks. Improving education outcomes will require Given the high levels of mobile phone access by exploring ways to make better use of educational Kenyan households and the relatively low cost of inputs. One major source of inefficiency is teacher electronic information dissemination, deploying absenteeism. A service delivery indicator study information technology innovations within the conducted by the World Bank in 2012 found that education sector for both student learning and an average of 45 percent of teachers were not monitoring outputs and outcomes, should be teaching during an unannounced visit to schools. explored. The majority of these absences were cases in which the teachers were not in class, even though With the increasing demand for secondary they were present in school (Figure 3.14) and school education as a result of free day head teachers were more likely to be absent than secondary education, it is increasingly important other teachers. These findings suggest very weak to address barriers to secondary attendance monitoring and accountability at the school level. for poor households. Currently, students from poor households are less likely than those from Information campaigns to increase active wealthier households to complete secondary or participation by parents in their children’s tertiary education, so policies that remove barriers education are one possible vehicle to improve to secondary education for the poor will help to governance and accountability within schools. boost completion rates. Evidence suggests that Greater involvement by parents might assist in scholarship programs targeted at poor households lowering teacher absenteeism rates, allowing can decrease the attendance gap in secondary better monitoring of teacher quality, and schooling (Demery and Gaddis, 2009). increasing the likelihood that disbursed funds are spent efficiently. Health Death and disability caused by the risks associated An opportunity exists to leverage information with poverty contribute heavily to Kenya’s overall technology for improved governance within burden of disease. Findings from the recent Figure 3.14: Poor facilities and overcrowding are the most common problems associated with schools Figure 3.15: Teacher absenteeism: At school but not teaching Percent of Kenyans reporting various problems with public Percentage of teachers by attendance status during schools, by wealth quintile unannounced visit 17% Demand for illegal payments 32% Absent from school Dirty Facilities 20% 16% 31% 43% Long Waiting Time 76% At school but not in 31% Absent Doctors 42% class 30% Lack of A ttention 50% In class but not 55% 27% 42% teaching Lack of Medicines 63% 18% In class and teaching 50% 3% Wealthiest Quintile Poorest Quintile Source: 2008 Afrobarometer Source: Kenya PETS++ 2012 June 2013 | Edition No. 8 49 Special Focus: Poverty Global Burden of Disease study indicate that 20 These responses reflect the challenge of ensuring percent of Kenya’s burden of disease (measured that people in rural areas have access to adequate in Disability Adjusted Life Years—the years of life facilities, and trained and motivated health lost due to early death and living with disability) workers. can be attributed to the following risks: childhood underweight; household air pollution; suboptimal Figure 3.16: Long waits and lack of medicines are the most common problems associated with health facilities breastfeeding; iron, vitamin A and Zinc deficiency; Percent of Kenyans reporting various problems with health sanitation and unimproved water. These risks facilities by wealth quintile in turn result from the day to day hardship that Demands for Illegal 7% 11% poverty brings such as food insecurity and the Payments Poor Facilities 25% lack of nutrition in diets, insufficient access to 43% information about the benefits of low cost healthy Overcroweded Classrooms 34% 43% behaviors (such as exclusive breastfeeding and Absent Teachers 14% 28% good continued feeding practices) and sub- Poor Teaching 19% 26% standard housing infrastructure. These findings 17% Lack of Textbooks suggest that many of the upstream causes of early 31% 8% death and disability in Kenya can be addressed in Too Expensive 28% large measure by one core prevention: alleviating Wealthiest Quintile Poorest Quintile poverty. Source: 2011 Afrobarometer As of 2010, Kenya’s total expenditure on health was 5.4 percent of GDP, below the SSA average of Expenditure targeted at upgrading facilities and 6.5 percent. Health expenditure as a share of GDP drug distribution networks at the health centre and as a share of total government spending (now and dispensary level is likely to have a great direct at 7.8 percent) has been increasing over time. impact on health care amongst the poor. Health Approximately 56 percent of Kenya’s total public centres and dispensaries are the major source of spending on health is provided by development primary level care for poor groups in rural areas partners (Kenya Public Expenditure Review, of Kenya. Historically, a high proportion of the 2010)—which more than doubled in the past funds intended for districts have failed to reach decade. them. As of 2007, only 67 percent of allocations to districts were received, and receipt of funds was Wide variation in per capita government spending often delayed (Public Expenditure Tracking Survey, and health personnel between counties indicates 2007). Further, the majority of these funds were wide disparities in the ability of public agencies spent at the district level, leaving peripheral at the local level to provide adequate health care. facilities with very limited operating funds. For example, Isiolo county spends KES 1,800 per With the new constitution, the responsibility of person on health, while Mandera spends less than primary health care, including the financing and KES 200. Uasin Gishu has upwards of 250 health management of health facilities will fall on county personnel per 100,000 people while Kilifi has less governments. than 40 (Government of Kenya, 2013). The Government of Kenya has established the Problems with care at health facilities are more Health Sector Services Fund (HSSF) to disburse frequently reported among the poorest Kenyans. operational funds directly to health centres When asked about problems with health facilities, and dispensaries, in an effort to improve the poorest Kenyans most commonly reported service delivery and accountability. The HSSF about long waits, lack of medicines, lack of was established in recognition of the fact that attention and high cost of services (Figure 3.15). inadequate access to resources is a contributor 50 June 2013 | Edition No. 8 Special Focus: Poverty to poor facility performance. HSSF resources certain clinical rotations in rural areas, can increase are channeled directly to each designated the likelihood that graduates choose to practice in facility’s bank account, and managed by a local rural areas. In addition, financial incentives (such health facility committee (HFC). The phased as hardship allowances, grants for housing or paid implementation of the HSSF began in October vacations) can offset opportunity costs of working 2010. While HFC members have the potential in rural areas. Improving the working conditions to improve accountability, many have not of rural health facilities (such as providing safe received any training in their roles or in facility working environments, supportive supervision management. Additionally, there is confusion and mentoring) can also encourage staff to take over HFC roles, with facility staff and HFC opportunities outside of urban centers (WHO, members expressing different opinions (Opwora 2010). et al., 2011). The potential of the HSSF to improve facility performance by directly channeling funds Infrastructure and involving communities in fund management Kenya’s utilities are largely inefficient; they are and prioritization is promising. This promise, characterized by high production costs, volatile however, is conditional on how it is implemented supply and losses in distribution. Access to and shaped in the context of devolution, where electricity and water is largely dependent on currently, the future management of the fund rainfall cycles. Within the power sector, 57 percent is uncertain, and more confusion between key of total power supply is generated through hydro stakeholders may arise. power. Kenya Power and Lighting Corporation (KPLC) reports transmission and distribution Demand-side voucher schemes to allow the losses of 18 percent, compared to a best practice poor in need of specialized medical care to of 10 percent, and captures only 85 percent of claim medical benefits, and subsidization of potential revenues (Kenya Public Expenditure transport costs from rural areas to national Review, 2010). These losses are an indication referral hospitals will help to reduce disparities of poor maintenance and rehabilitation of the in access to specialized healthcare. Funding to network. Within the water sector, water utilities pay for emergency referral transport is low, and are capturing less than 60 percent of the revenues disproportionately excludes poor individuals from they need to operate effectively, largely a result of receiving specialized treatment. National referral underpricing and high non-payment rates (Kenya hospitals are concentrated in urban areas, and Public Expenditure Review, 2010). the highest concentration of medical specialists is found in Nairobi. Poor individuals in need of Policies to increase the water storage capacity specialized treatment, thus need to travel to the of utilities will lower susceptibility to Kenya’s closest referral hospital with adequate facilities. rainfall cycles. Within the water sector, investment to exploit water harvesting during Incentive programs and policies to recruit and heavy rainfall cycles, for example through artificial retain health workers in rural and remote areas dams, will result in lower vulnerability during can help to lower the gap in health care provision periods of drought. Within the electricity sector, between urban and rural areas. Health workers diversification of power sources to geothermal tend to prefer working in urban areas, resulting and wind power will lower dependency on hydro in an undersupply of workers in rural and remote power and lower the incidence of rationing during areas. There is evidence that a mix of strategies can dry seasons. The government has embarked on help to attract trained health staff to these areas. an ambitious plan to increase power supply within For example, locating professional schools and Kenya. residency programs outside of capitals or having June 2013 | Edition No. 8 51 Special Focus: Poverty Government subsidies to lower or eliminate To achieve the first Millennium Development electricity connection costs for poor households Goal, poverty headcount would need to drop by may increase uptake of electricity connections 20 percentage points between 2005 (43 percent) amongst poor households. Within slum areas, and 2015 (estimated target of 23 percent). KPLC already charges a reduced connection fee Broadly speaking, poverty reduction can be of KES 1,000, which is highly subsidized over the achieved through two complimentary channels: regular connection fee of KES 35,000 (Kenya Public growth and redistribution in favor of the poor Expenditure Review, 2010). However, the fee (reduction in inequality). Figure 3.16 shows the remains restrictive for the poorest households. combinations of growth (horizontal axis) and redistribution (vertical axis) that would allow Improved governance and accountability the attainment of MDG1 . In a scenario without systems within utilities can set the foundation redistribution (and as a result no reduction in for improved service delivery. Poor governance inequality) household consumption levels would remains an issue for service delivery within the need to grow by 49 percent in real terms between water and electricity sectors. Historically, these 2005 and 2015 to attain MDG1. This corresponds sectors have been characterized by high rates of to annual consumption growth of 4.1 percent unaccounted losses, poor revenue collection rates, (horizontal intercept in Figure 5.1). To put this into low levels of maintenance and mismanagement. perspective, annual growth in per capita GDP in Improving performance through increased Kenya averaged 1.9 percent between 2005 and oversight and accountability can help overcome 2011. Even in the case of a moderate reduction these challenges. in inequality (an annual decline in inequality of 0.5 percent-the average for SSA), household 3.5 What will it take to make poverty history? consumption levels would need to grow at more T he dream of a Kenya free of extreme poverty is attainable. Experience from the region shows that achieving rapid reductions in extreme than 3 percent per year to attain the MDG poverty target by 2015. In a case of no growth in household consumption whatsoever, the Gini coefficient poverty is possible, the challenge for Kenya and would have to drop from 0.47 in 2005 to 0.33 in other countries in the region is to sustain these 2015, an annual decline of 3.5 percent . gains and to make poverty reduction a national priority. Shaping the post-2015 agenda, the Development Committee of the World Bank Group recently Available evidence suggests that Kenya’s endorsed the goal of eradicating extreme poverty progress on poverty reduction between the early Figure 3.17: Reaching the first Millennium Development Goal is unlikely 1990s and 2005 was negligible. Consequently, Combinations of growth and inequality reduction necessary to Kenya is unlikely to attain the first Millennium attain MDG1between 2005 and 2015 Development Goal (MDG1) of reducing by half 4 Annual Pace of Inequality Reduction (percent) the proportion of the population living below 1.25 3.5 dollars per day (extreme poverty) between 1990 3 and 2015. In 2005, 43.4 percent of Kenyans were 2.5 classified as extreme poor by this international 2 standard. There is however, no reliable estimate 1.5 for extreme poverty in 1990—the baseline year 1 of MDG1. Using the national accounts based 0.5 simulation presented in Section 2, and assuming 0 distribution-neutral growth, a ballpark estimate of 0 1 2 3 4 5 extreme poverty in 1990 is 45.6 percent. Annual Growth in Household Consumption (percent) Source: World Bank 52 June 2013 | Edition No. 8 Special Focus: Poverty Box 3.6: Turbocharging Poverty Reduction: The Case of Rwanda How changes in inequality hamper or boost poverty reduction The importance of inequality dynamics in boosting or complicating poverty reduction is nicely illustrated by Rwanda’s experience over the past decade. Between 2001 and 2006, household consumption in Rwanda grew at a solid pace of 2 percent per year, resulting in a real consumption gain of over 10 percent. However, poverty dropped by only two percentage points (59 percent in 2001 and 57 percent in 2006). The disappointing performance in terms of poverty reduction during this period is explained by rising inequality: If inequality had remained constant, poverty would have fallen by more than 5 percentage points instead of the 2 points actually observed. In the subsequent five years (2006-2011), household consumption in Rwanda grew at 3 percent per annum, and the incidence of poverty fell by 12 percentage points. Although strong, growth in household consumption accounted for “only� 8.5 percentage points of the reduction in poverty. The decrease in inequality added another 3.5 percentage points-more than the total poverty reduction in the preceding period. Source: World Bank within a generation globally. This goal has been Despite the large reduction in poverty needed to specified as reducing the proportion of people eliminate extreme poverty, it is not beyond reach. living on less than $1.25 per day to no more than For instance, a reduction in inequality of 1 percent 3 percent by 2030. In Kenya, this entails reducing per year coupled with an annual consumption poverty by 40 percentage points between growth rate of 2.1 percent would suffice to hit 2005 and 2030. What will it take for Kenya to the target (Figure 3.17). With annual growth of achieve this goal? Figure 3.17 shows the possible 2.5 percent, inequality will need to decline at 0.8 combinations of growth and redistribution percent per year to achieve the goal. If inequality necessary by to attain the target. In the absence of could be reduced by 1.5 percent per year, a redistribution, household consumption will need modest consumption growth rate of 1.2 percent to increase almost four-fold between 2005 and per annum would be enough to hit the target. 2030, requiring an annual consumption growth of 5.6 percent. With an average decline in inequality These simulations highlight the important of 0.5 percent per year, consumption will still mediating role played by inequality dynamics in need to grow by 3.2 percent per annum to attain poverty reduction (see Box 3.6). It is clear that in the target, a daunting task given Kenya’s modest the absence of redistribution, it will be hard for performance during the past decades. Kenya to attain growth rates that are sufficiently high, to make significant dents in poverty and Figure 3.18: What Will it Take for Kenya to Reduce Extreme Poverty to 3 Percent by 2030? reach the 2030 target. The scope for rapid poverty Combination of growth and inequality reduction necessary reduction will crucially depend on the extent to to reduce extreme poverty to 3 percent by 2030 which the Government can bring down inequality 3 levels by adopting policies that are more likely to Annual Pace of Inequality Reduction 2.5 result in pro-poor growth, ensuring good basic services are available everywhere and designing 2 effective social protection mechanisms (Box 3.7). 1.5 3.6 Poverty reduction: the way forward T 1 he analyses presented in this report suggest 0.5 the following areas as possible elements of a 0 poverty-reduction agenda: 0 1 2 3 4 5 6 Annual Growth in Household Consumption (percent) Source: World Bank June 2013 | Edition No. 8 53 Executive Summary Box 3.7: Strengthening Social Protection in Kenya Why cash transfers not food aid should become the backbone of social assistance for the poor in Kenya Kenya’s commitment to social protection is encoded in the constitution and in the National Social Protection Policy (NSPP) of 2012. Social protection programs in Kenya fall under three categories: social security, social health insurance and social assistance (or safety nets). As a majority of the people who benefit from social security (pensions) and health insurance are formal sector workers, social assistance programs are most relevant for the poor and vulnerable. Emergency food aid dominates social assistance The most common type of social assistance programs are food programs—emergency food aid in response to droughts or floods and school feeding programs. These programs jointly absorb over 80 percent of all safety net beneficiaries, and make up over half of all safety net spending. Five social cash transfer programs targeting orphans, the disabled, older persons and the food insecure make up 13 percent of all safety net beneficiaries. Support through emergency food aid, while important, is sporadic, unpredictable and ineffective in reducing chronic poverty. Cash transfers on the other hand provide regular and predictable income support, and have played a key role in reducing poverty and improving health and education outcomes among the poor in many countries around the world (See Box 3.4). Scale up, harmonize and strengthen cash transfers While the number of people covered by cash transfer programs has increased since 2005, they only reach about 8 percent of the poor population. Kenya’s cash transfer programs are small, fragmented and unable to respond when shocks hit. Improving the effectiveness of these programs will require ramping up government spending on them, improving coordination among implementing agencies, and building capacity for programs to respond to shocks, using early warning systems and contingency funds that can mobilize additional resources when needed. The government is seeking to implement these recommendations through the establishment of the National Safety Net Program. Source: World Bank Figure 3.18: Pillars of Social Protection in Kenya (5) Investing in a system of routine household Pillars of Social Protection in Kenya budget surveys to monitor poverty and I. Social Security inequality II. Social Health Insurance National Social Security Fund National Hospital Insurance Fund Civil Service Pension Fund Poverty reduction needs sustained economic growth, but the nature of that growth also III. Social Assistance (Safety Nets) matters. If Kenya’s growth and job creation is only Food Aid Programs Cash Transfer Programs General food distribution Orphans & Vulnerable Children (CT-OVC) concentrated at the high-skills end—for example Supplementary feeding Regular school meals Hunger Safety Net Program (HSNP) in industries that hire software engineers or Urban Food Subsity Program (UFSP) Home grown school meals Persons with severe disability cash transfer bankers, the poverty reducing effects of growth Older Persons Cash Transfer (OPCT) Food/ Cash for Assets will be limited. However, if more productive jobs become increasingly available to people with Source: World Bank low and medium skills—these jobs will represent (1) Fostering pro-poor economic growth and job pathways out of poverty. To encourage the creation growth of low and middle skills jobs, especially (2) Enhancing the productivity of smallholder in manufacturing, the government can work to farms incentivize exports and improve the investment (3) Using public spending to make key and business environment more broadly. opportunities available to Kenyans of all backgrounds Pro-poor growth cannot be achieved without (4) Strengthening and expanding the cash modernizing smallholder farming. Since the transfer programs that protect and provide majority of Kenya’s poor depend on smallholder income support to the poor agriculture for their livelihood, increasing their 54 June 2013 | Edition No. 8 Special Focus: Poverty productivity through the use of fertilizer, improved been historically neglected (such as remote rural seeds and access to markets for agricultural and arid areas) and building transparency and production, will drive poverty reduction in the accountability to ensure that these resources are short to medium term. used effectively. Poverty reduction can be complemented with A substantial part of inequality in Kenya can be greater equity in Kenyan society and achieved explained by inequality in labor income, which is in part through stronger cash transfer programs, in turn determined by inequality in access to and and more equitable and effective public quality of education. Ensuring that children from spending. Cash transfer programs worldwide all walks of life have access to quality education have been shown to help reduce poverty and will expand the pool of skilled workers, which vulnerability, through predictable income support would have positive effects on poverty reduction, that studies show that households use to improve both through a growth effect (skilled workers their consumption, to invest in productive assets earn more) and an inequality effect (having a and meet their health and education goals. Kenya higher supply of skills would drive down the skills has a host of cash transfer programs that reach premium and reduce inequality). a very small share of the population in need, are fragmented and not able to respond to shocks. Kenya’s inconsistent record of monitoring poverty Strengthening and expanding these programs with and the importance of understanding the nature their increased prioritization in the government of changes in growth and inequality, calls for a budget, should be a central pillar of Kenya’s systematic program of rigorous household data poverty reduction strategy (Box 3.7). collection. As suggested by the simulations, the effects future growth will have on poverty will Leveling the playing field in access to key largely depend on whether inequality increases opportunities—such as quality education, energy, or decreases, and the pace of its change. Simply water and sanitation—has the potential not only measuring economic growth through national to boost growth, but also to reduce inequality. accounts, will not convey useful or reliable Circumstances such as family wealth, ethnicity information on the evolution of poverty. To or the geographic location of the household convincingly monitor the impact of Government play an oversized role in influencing access to policies on household consumption growth, opportunities that can enhance the health, equity and poverty reduction, comprehensive education and overall well-being of children in and comparable household surveys need to Kenya. These circumstances should not matter be implemented regularly. This will not only and public spending should work to remove determine whether progress is being made, but their influence, so that every child has the same will also identify areas where policies need to be chance to seize the opportunities being generated adapted or stepped up, to maximize their impact in a growing Kenya. This will require focusing on poverty reduction. financial and human resources in areas that have June June 2013 2013 | Edition No. | Edition No. 8 8 53 55 REFERENCES ▪ Alkire, S., & Roche, J. M. (2013). “How Multidimensional Poverty Went Down: Dynamics and Comparisons�. ▪ Christiaensen, L., Lanjouw, P., Luoto, J., & Stifel, D. (2011). “Small Area Estimation-Based Prediction Methods to Track Poverty Validation and Applications�. Washington, DC. ▪ Datt, G., Ramadas, K., Mensbrugghe, D. V. D., Walker, T., & Wodon, Q. (2002). “Predicting the effect of aggregate growth on poverty. Toolkit for Evaluating the Poverty and Distributional Impact of Economic Policies�. Washington, DC. ▪ Demombynes, G. (2012). “What Has Driven the Decline of Infant Mortality in Kenya? Policy Research Working Paper.� Washington, DC. ▪ Demombynes, G., &Hoogeveen, J. G. (2007). “Growth, Inequality and Simulated Poverty Paths for Tanzania, 1992-2002�. ▪ Demombynes, G., &Thegeya, A. (2012).Kenya ’ s Mobile Revolution and the Promise of Mobile Savings. World Bank Policy Research Working Paper. Washington, DC. ▪ Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2002). “Micro-Level Estimation of Welfare. Washington, DC. doi:10.1596/1813-9450- 2911�. ▪ Government of Kenya (2013). “Public Expenditure Review. Nairobi�. ▪ Jones C (2003): Aggregate and Sector Import Price Elastcities for a sample of African Countries’ CREDIT Research Paper No 08/03. ▪ Journal of African Economies, 16(4), 596–628. doi:10.1093/jae/ejm002. ▪ Kee, H. L. Nicita A., and Olarreaga (2008): Import Demand Elastcities and Trade distortions� Review of Economics and Statistics Vol 90, No4, pp 666-682. ▪ Lim SS et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet. 2012 Dec 13; 380: 2224–2260. ▪ Lustig, N., Lopez-calva, L. F., & Ortiz-Juarez, E. (2012). “Declining Inequality in Latin America in the 2000s. World Bank Policy Research Working Paper, (October)�. ▪ Opwora, A., Toda, M., Waweru, E., Edwards, T., Fegan, G., & Noor, A. (2011). “Health Service Delivery , Governance and Supportive Supervision under the Health Sector Services Fund National Baseline Survey Volume I: Main Report (Vol. I)�. ▪ Ravallion, M. (2004). “Pro-Poor Growth: A Primer�. ▪ Senhadji, Semlali 1997: Time series of Structural Import demand Equations- A cross country Analysis, IMF Working Paper WP/97/132. ▪ Tokarick S (2010): A Method for Calculating Export Supply and Import Demand Elasticities, IMF Working Paper WP/10/108. ▪ UNDP. (2013). “Kenya Country Profile: Human Development Indicators�. Retrieved May 27, 2013, from http://hdrstats.undp.org/ en/countries/profiles/KEN.html. ▪ WHO. (2010). “Increasing access to health workers in remote and rural areas through improved retention - Policy Recommendations�. Geneva. ▪ World Bank.(2013). “Rwanda Economic Update�. 56 June 2013 | Edition No. 8 ANNEXES Annexes Annex 1: Macroeconomic environment 2008 2009 2010 2011 2012 GDP Growth Rates (percent) 1.5 2.7 5.8 4.4 4.6 Agriculture -4.3 -2.5 6.3 1.5 3.8 Industry 4.7 2.8 5.4 2.9 4.5 Services 2.7 6.7 3.8 5.2 4.6 Fiscal Framework (percent of GDP) Total Revenue 21.8 21.9 23.8 23.8 22.8 Total Expenditure 27.2 27.9 29.7 29.2 28.9 Grants -0.1 -0.1 -0.1 0.0 0.5 Budget Deficit (incl grants) -4.3 -5.2 -5.1 -4.3 -5.6 Total Debt 45.6 47.5 49.9 48.5 45.2 External Account (percent of GDP)* Exports (fob) 18.7 14.4 16.5 17.1 15.1 Imports (cif) 42.5 32.8 39.1 43.5 40.3 Balance of Trade -15.7 -12.4 -14.7 -18.9 -17.0 Current Account Balance -7.3 -5.3 -7.9 -9.8 -11.2 Financial and Capital Account 5.6 7.8 8.4 9.7 14.2 Overall Balance -1.7 2.5 0.5 -0.1 3.0 Inflation (average) 16.2 10.5 4.1 14.0 9.6 Exchange Rate (Average KES/$) 69.2 77.4 79.2 88.8 84.5 Source: KNBS, IMF and CBK * As at 31 December 2012 Annex 2: GDP Growth Rates 2008-2012 Kenya SSA EAC 2008 2009 2010 2011 2012 2008-2012 Kenya 1.5 2.6 5.6 4.5 4.6 3.8 SSA (excluding South Africa) 6.1 4.0 6.1 5.3 5.8 5.5 Uganda 8.7 7.2 5.9 6.7 3.4 6.4 Tanzania 7.4 6.0 7.0 6.3 6.5 6.7 Rwanda 11.2 4.1 7.2 8.6 7.7 7.8 Source: World Bank Global Economic Prospects 2013 58 June 2013 | Edition No. 8 Annexes Annex 3: Kenya annual GDP Years GDP, constant GDP/capita, GDP, current prices GDP Growth prices current prices Kshs (Billions) Kshs (Billions) U.S. dollars Percent change 2000 968 965 399 0.6 2001 1026 1011 413 4.7 2002 1039 1014 408 0.3 2003 1142 1042 456 2.8 2004 1274 1090 478 4.6 2006 1623 1229 637 6.3 2007 1834 1315 749 7.0 2008 2108 1357 813 1.5 2009 2367 1394 793 2.7 2010 2554 1475 810 5.8 2011 3049 1540 833 4.4 2012 3440 1610 991 4.6 Source: KNBS June 2013 | Edition No. 8 59 60 Annex 4: Quartely growth rates (percent) AGRICULTURE INDUSTRY SERVICES GDP (Q:Q-3)/ (Q:Q-3)/ (Q:Q-3)/ (Q:Q-3)/ Years Quarters Q/Q-1 Q/Q-4 Q/Q-1 Q/Q-4 Q/Q-1 Q/Q-4 Q/Q-1 Q/Q-4 (Q-4:Q-7) (Q-4:Q-7) (Q-4:Q-7) (Q-4:Q-7) 2007 1 -15.5 8.7 6.5 -2.2 3.8 4.6 3.5 5.6 5.9 -2.8 7.1 6.6 2 -9.6 2.0 5.8 5.7 9.3 5.9 2.8 9.3 6.3 0.1 8.3 7.1 June 2013 | Edition No. 8 3 24.8 -0.1 4.0 3.2 8.9 6.9 4.7 8.5 6.6 9.1 6.3 6.6 4 4.7 -0.2 2.3 -0.5 6.2 7.1 -2.0 9.1 8.1 0.1 6.4 7.0 2008 1 -19.7 -5.2 -0.9 -5.3 2.8 6.8 -3.1 2.3 7.2 -7.5 1.1 5.5 2 -4.6 0.1 -1.3 9.2 6.2 6.0 3.4 2.9 5.6 1.2 2.2 4.0 3 18.1 -5.2 -2.7 3.4 6.4 5.4 5.7 3.8 4.5 9.5 2.6 3.1 4 4.0 -5.9 -4.3 -3.5 3.2 4.7 -4.1 1.7 2.7 -2.1 0.2 1.5 2009 1 -16.0 -1.5 -3.4 -1.4 7.5 5.8 5.1 10.3 4.6 -2.1 6.2 2.7 2 -6.9 -3.9 -4.3 3.6 1.9 4.7 -2.6 3.8 4.9 -2.9 1.9 2.7 3 18.9 -3.3 -3.8 0.1 -1.3 2.7 8.3 6.4 5.5 9.5 1.9 2.5 4 6.2 -1.3 -2.5 1.4 3.7 2.8 -4.1 6.3 6.7 -2.8 1.2 2.7 2010 1 -9.9 5.9 -0.8 -0.7 4.4 2.1 -0.9 0.2 4.1 -1.9 1.4 1.6 2 -9.2 3.3 0.8 4.2 5.0 2.9 2.7 5.7 4.6 1.5 6.1 2.6 3 25.0 8.6 4.0 2.2 7.2 5.1 8.8 6.1 4.6 10.7 7.2 4.0 4 4.6 7.0 6.3 -0.8 4.8 5.4 -6.6 3.3 3.8 -1.8 8.3 5.8 2011 1 -15.6 0.2 4.9 -0.6 5.0 5.5 1.5 5.8 5.2 -5.0 4.8 6.6 2 -5.7 4.0 5.1 1.9 2.7 4.9 1.3 4.4 4.9 0.3 3.5 6.0 3 20.5 0.3 2.9 0.5 1.0 3.3 9.6 5.2 4.7 11.2 4.0 5.1 4 6.4 2.0 1.5 1.3 3.1 2.9 -6.5 5.3 5.2 -0.7 5.2 4.4 2012 1 -15.6 2.1 2.0 -0.5 3.2 2.5 0.6 4.5 4.9 -6.1 4.0 4.2 2 -5.7 2.1 1.6 1.3 2.6 2.5 1.4 4.5 4.9 0.6 4.4 4.4 3 24.9 5.8 3.1 1.3 3.4 3.1 9.3 4.3 4.6 11.4 4.5 4.5 4 5.3 4.7 3.8 6.3 8.5 4.5 -5.7 5.2 4.6 0.0 5.3 4.6 Source: World Bank Calculations based on KNBS data. Agriculture = Agriculture and forestry + Fishing Industry = Mining and quarrying + Manufacturing + Electricity ans water + Construction Servics = Wholesale and retail trade + Hotels and restaurants + Transport and communication + Financial intermediation + Real estate, renting and business services + Public administration + Education + Other services + FISIM Annexes Annexes Annex 5: Inflation Year Month Overall Inflation Food Inflation Energy Inflation Core Inflation 2011 January 5.4 8.6 5.7 1.4 February 6.5 9.8 7.8 1.8 March 9.2 15.1 9.6 2.5 April 12.1 19.1 12.7 3.6 May 13.0 20.1 14.4 4.0 June 14.5 22.5 15.5 4.8 July 15.5 24.0 16.2 5.6 August 16.7 23.9 16.8 8.5 September 17.3 24.4 17.6 9.1 October 18.9 26.2 19.2 10.4 November 19.7 26.2 20.6 11.8 December 18.9 25.0 19.7 11.6 2012 January 18.3 24.6 17.3 12.1 February 16.7 22.1 14.8 12.1 March 15.6 20.3 13.0 12.0 April 13.1 16.2 11.1 11.0 May 12.2 14.6 10.0 11.3 June 10.1 10.5 9.0 10.7 July 7.7 6.6 7.4 9.7 August 6.1 3.6 6.7 9.0 September 5.3 2.9 6.0 8.3 October 4.1 1.4 5.0 7.0 November 3.3 1.7 3.1 5.5 December 3.2 1.7 2.8 5.5 2013 January 3.7 2.4 3.9 5.2 February 4.5 4.0 4.6 4.9 March 4.1 2.9 5.3 4.8 April 4.1 3.6 4.3 4.6 May 4.1 4.3 3.5 4.1 Source: World Bank Calculations based on KNBS data June 2013 | Edition No. 8 61 Annexes Annex 6: Tea production and exports Year Month Production Price Exports Exports value MT Ksh/Kg MT Ksh Million 2011 January 35,999 256 31,110 7,871 February 26,711 251 28,814 7,223 March 22,459 243 35,852 8,890 April 31,482 241 32,084 7,900 May 32,856 245 31,898 7,825 June 28,955 264 34,957 7,825 July 26,343 283 33,629 8,907 August 24,471 294 32,693 9,266 September 30,493 292 26,430 9,333 October 39,926 291 29,422 7,686 November 36,825 269 33,353 8,855 December 41,393 251 35,187 9,334 2012 January 36,205 250 35,382 9,145 February 18,412 245 37,656 9,123 March 17,859 251 31,280 9,415 April 18,118 256 26,816 7,804 May 37,383 264 25,060 6,445 June 30,197 279 29,148 7,770 July 24,306 288 28,054 7,813 August 31,920 288 30,996 8,798 September 33,549 280 30,689 8,771 October 40,235 272 33,167 9,448 November 39,977 277 38,681 10,840 December 41,401 281 30,067 8,463 2013 January 45,390 284 40,190 11,383 February 38,503 271 34,585 10,071 March 33,368 241 32,534 8,619 April 45,390 284 Source: KNBS 62 June 2013 | Edition No. 8 Annexes Annex 7: Coffee production and exports Year Month Production Price Exports Exports value MT Ksh/Kg MT Ksh Million 2011 January 3,774 682 3,067 1,282 February 3,851 640 3,261 1,671 March 3,639 587 4,204 2,155 April 2,298 474 4,254 2,294 May 0 0 3,878 1,963 June 1,136 596 2,677 1,322 July 3,305 592 2,857 1,749 August 4,558 582 3,096 1,955 September 2,904 593 3,317 2,161 October 1,388 543 3,298 2,134 November 1,331 541 1,990 1,173 December 1,800 603 1,672 940 2012 January 4,770 544 3,094 1,454 February 6,505 369 3,668 1,937 March 3,317 389 5,069 2,550 April 4,801 342 4,625 2,369 May 5,472 303 4,924 2,275 June 3,884 258 4,887 2,098 July 3,086 298 5,727 2,397 August 3,948 277 4,484 1,712 September 4,474 265 4,421 1,596 October 2,924 263 4,482 1,690 November 1,794 272 4,110 1,453 December 1,075 308 2,223 740 2013 January 3,938 344 2,790 1,062 February 4,825 320 3,955 1,429 March 4,074 327 3,179 1,188 April 6,038 279 Source: KNBS June 2013 | Edition No. 8 63 Annexes Annex 8: Horticulture exports Year Month Exports Exports value MT Ksh. Million 2011 January 16,231 7,470 February 17,531 7,368 March 21,287 7,548 April 23,448 7,159 May 21,839 8,315 June 17,730 6,836 July 15,420 5,531 August 16,128 6,582 September 15,658 6,745 October 17,553 9,508 November 17,277 6,647 December 16,145 8,915 2012 January 14,974 8,721 February 16,053 6,726 March 18,967 6,515 April 17,408 6,317 May 17,027 6,013 June 15,271 6,227 July 17,349 7,813 August 15,869 5,825 September 16,506 7,567 October 19,708 11,368 November 18,347 7,742 December 18,250 9,036 2013 January 18,398 9,071 February 21,576 9,198 March 19,814 7,061 April 19,790 5,228 Source: KNBS 64 June 2013 | Edition No. 8 Annexes Annex 9: Local electricity generation by source (Million KWh) Year Month Hydro Geo-thermal Thermal Total 2011 January 296 119 188 603 February 246 105 200 551 March 259 126 225 610 April 237 120 224 582 May 264 124 222 610 June 268 118 200 586 July 263 122 226 611 August 254 125 234 614 September 249 121 224 595 October 253 122 225 601 November 263 115 208 587 December 331 125 156 613 2012 January 330 129 169 627 February 332 125 159 616 March 293 134 194 620 April 273 124 175 572 May 323 132 159 615 June 342 129 147 618 July 358 119 168 646 August 348 122 176 645 September 358 119 168 646 October 360 129 166 654 November 372 121 159 652 December 369 130 148 647 2013 January 377 129 169 675 February 333 113 160 606 March 348 135 160 645 April 345 152 140 637 Source: KNBS June 2013 | Edition No. 8 65 Annexes Annex 10: Soft drinks and sugar production Galvanized Year Month Soft Drinks Sugar Cement Sheets "000" litres MT MT MT 2011 January 34,446 55,974 22,094 364,432 February 32,457 52,069 22,386 335,247 March 36,156 53,842 22,928 355,858 April 31,162 52,061 20,957 363,035 May 26,622 49,130 24,744 376,246 June 28,910 38,818 24,677 365,494 July 28,478 25,884 24,906 393,149 August 28,580 26,060 24,659 405,546 September 29,674 22,815 17,988 407,838 October 28,540 28,990 16,619 361,941 November 27,366 32,689 22,104 364,789 December 38,962 36,729 24,033 384,853 2012 January 34,317 53,852 22,940 350,615 February 32,009 49,480 19,655 378,453 March 37,363 52,342 21,507 397,009 April 29,331 44,914 20,892 360,540 May 24,359 40,503 22,197 381,026 June 27,391 45,111 17,180 396,951 July 22,073 41,607 21,411 398,458 August 24,458 37,058 23,040 399,873 September 31,113 32,503 23,268 382,141 October 32,540 30,123 20,473 421,579 November 31,497 31,886 21,969 415,866 December 33,067 34,651 21,283 357,212 2013 January 34,246 49,046 .. 387,527 February .. 50,036 .. 377,561 March .. 43,647 .. 373,337 April .. .. .. 375,237 Source: KNBS 66 June 2013 | Edition No. 8 Annexes Annex 11: Tourism arrivals Year Month JKIA MIA TOTAL 2011 January 79142 35770 114912 February 69221 31211 100432 March 71734 26027 97761 April 66276 10181 76457 May 74148 5167 79315 June 72944 6676 79620 July 131519 12037 143556 August 113438 23402 136840 September 85397 17317 102714 October 88918 18741 107659 November 89394 19641 109035 December 94355 21624 115979 2012 January 83450 28134 111584 February 80405 24636 105041 March 75668 19965 95633 April 72023 7531 79554 May 71287 4830 76117 June 90972 5934 96906 July 108136 12671 120807 August 108869 17771 126640 September 90153 13312 103465 October 95911 12942 108853 November 83122 16135 99257 December 92365 23290 115655 2013 January 85838 26446 111984 February 48970 24031 73001 March 52103 17850 69953 Source: KNBS June 2013 | Edition No. 8 67 Annexes Annex 12: New vehicle registration Year Month All body types 2011 January 18805 February 16190 March 16497 April 12560 May 15115 June 21546 July 19128 August 18797 September 16802 October 17202 November 17640 December 15559 2012 January 13730 February 12693 March 13066 April 8257 May 16652 June 15091 July 22577 August 16970 September 12003 October 15449 November 14867 December 11689 2013 January 20997 February 16928 March 17061 Source: KNBS 68 June 2013 | Edition No. 8 Annexes Annex 13: Exchange rate Year Month USD UK POUND EURO 2011 January 81.0 127.7 108.2 February 81.5 131.5 111.3 March 84.2 136.1 117.9 April 83.9 137.1 121.1 May 85.4 139.5 122.4 June 89.0 144.4 128.1 July 89.9 145.0 128.5 August 92.8 151.9 133.0 September 96.4 152.1 132.7 October 101.3 159.4 138.7 November 93.7 148.2 127.1 December 86.7 135.1 114.1 2012 January 86.3 133.9 111.4 February 83.2 131.4 110.1 March 82.9 131.2 109.6 April 83.2 133.2 109.6 May 84.4 134.3 108.0 June 84.8 132.0 106.5 July 84.1 131.2 103.5 August 84.1 132.1 104.2 September 84.6 136.3 108.8 October 85.1 136.8 110.3 November 85.6 136.8 109.9 December 86.0 138.8 112.8 2013 January 86.9 138.8 115.5 February 87.4 135.5 116.9 March 85.8 129.4 111.3 May 84.1 128.8 109.2 Source: CBK June 2013 | Edition No. 8 69 Annex 14: Interest rates 70 Year Short Term Long term Average Overall Interest Interbank 91-Tbill CBR Deposit Savings Weighted rate Rate Lending Spread* Rate 2011 January 1.0 2.0 6.0 3.4 1.3 14.0 10.6 February 1.0 3.0 5.8 3.4 1.4 13.9 10.5 June 2013 | Edition No. 8 March 1.0 3.0 6.0 3.5 1.4 13.9 10.4 April 4.0 3.0 6.0 3.5 1.4 13.9 10.5 May 6.0 5.0 6.3 3.6 1.4 13.9 10.3 June 6.0 9.0 6.3 3.7 1.4 13.9 10.2 July 9.0 9.0 6.3 3.9 1.4 14.1 10.3 August 14.0 9.0 7.0 4.1 1.4 14.3 10.3 September 7.0 12.0 7.0 4.2 1.3 14.8 10.6 October 15.0 15.0 11.0 4.8 1.3 15.2 10.4 November 29.0 16.0 16.5 5.7 1.4 18.5 12.7 December 22.0 18.0 18.0 7.0 1.6 20.0 13.1 2012 January 19.0 21.0 18.0 7.7 1.6 19.5 11.9 February 18.0 20.0 18.0 8.0 1.7 20.3 12.3 March 24.0 18.0 18.0 8.0 1.7 20.3 12.3 April 16.0 16.0 18.0 9.0 1.6 20.2 11.2 May 17.0 11.0 18.0 8.4 1.6 20.1 11.7 June 17.0 10.0 18.0 7.9 1.5 20.3 12.4 July 13.7 12.0 16.5 8.3 1.7 20.2 11.9 August 9.0 10.9 13.0 7.8 1.6 20.1 12.3 September 7.0 7.8 13.0 7.4 1.6 19.7 12.3 October 9.1 9.0 13.0 6.9 1.6 19.0 12.2 November 7.1 9.8 11.0 6.7 1.6 18.7 12.1 December 5.8 8.3 11.0 6.8 1.6 18.1 11.3 2013 January 5.9 8.1 9.5 6.5 1.6 18.1 11.6 February 9.3 8.4 9.5 6.3 1.6 17.8 11.6 March 8.9 9.9 9.5 6.5 1.4 17.8 11.2 Annexes April 7.9 10.4 8.5 6.4 1.4 17.9 11.5 Source: CBK and World bank *World Bank computations Annex 15: Credit to private sector Annexes Sector Annual Building & Transport and Finance And Mining & Households Year Month Growth Rates Total Private Agriculture Manufacturing Trade Construction Communication Insurance Real Estate Quarrying Private Durables Consumer Business Services Other Activities 2011 January 21.04 20.75 30.28 33.52 -1.87 -5.54 -8.24 96.24 -11.97 17.02 15.18 34.38 -7.13 February 23.30 17.36 27.40 29.46 -2.17 2.27 -19.10 93.56 23.23 21.58 17.81 18.10 18.77 March 25.70 20.78 26.57 28.84 1.08 -3.03 -22.93 95.12 59.53 20.53 16.88 25.34 35.77 April 27.28 23.98 26.61 22.08 3.89 2.77 -13.97 96.58 53.71 23.55 50.22 21.02 22.74 May 27.40 21.41 28.41 29.04 15.14 3.85 -6.45 62.99 45.91 22.03 27.25 13.77 49.25 June 30.71 27.72 30.55 30.62 27.11 13.62 2.43 44.47 56.05 29.29 30.14 13.58 59.84 July 32.41 35.85 28.52 39.45 38.69 26.15 26.86 47.17 40.04 27.38 33.53 8.99 41.20 August 32.70 33.14 29.02 37.52 36.48 29.21 25.82 42.36 45.25 33.76 37.55 4.49 43.86 September 36.27 37.97 38.60 40.08 55.58 28.52 23.79 37.74 46.62 39.97 38.58 4.35 50.07 October 35.21 28.84 39.61 37.81 53.37 32.63 24.53 35.72 45.59 39.91 35.84 3.41 48.84 November 32.50 25.35 28.06 34.70 48.49 46.50 26.01 37.49 77.83 34.68 33.61 -0.71 40.36 December 30.87 27.58 30.28 24.26 55.67 45.27 31.18 38.99 73.29 32.54 26.66 -5.76 47.42 2012 January 28.04 24.73 24.84 27.37 54.17 40.88 14.15 38.34 93.67 24.44 21.34 -15.39 53.72 February 26.05 21.08 22.21 26.27 65.23 31.14 22.93 39.43 28.27 17.36 19.23 -0.49 40.17 March 24.01 16.56 30.52 24.00 54.38 36.32 28.40 36.69 18.02 17.40 19.91 -4.97 23.73 April 22.61 14.54 29.67 27.38 59.37 24.97 19.26 29.02 37.88 15.67 -7.37 -5.74 47.60 May 21.76 14.29 26.94 25.40 51.81 28.68 17.87 29.66 10.03 13.04 16.29 0.01 25.01 June 16.14 10.08 23.40 21.37 49.92 10.31 9.97 27.76 1.76 7.00 14.74 -5.07 16.23 July 13.46 3.59 19.03 10.48 36.74 -2.91 10.71 26.44 3.34 7.72 13.69 -1.33 26.97 August 11.92 3.93 14.82 7.80 35.22 -2.71 16.22 26.24 -10.44 8.13 12.47 0.53 21.75 September 7.72 0.73 7.28 3.19 27.80 -4.27 20.25 24.84 -13.71 5.95 7.97 0.89 8.82 October 7.12 3.65 3.57 2.79 32.48 -2.58 21.93 22.67 -23.96 5.40 4.52 2.23 10.95 November 9.07 6.68 10.23 4.82 36.98 -10.32 15.43 21.09 -23.80 7.60 6.66 8.21 15.02 December 10.42 8.06 15.78 10.63 36.17 -13.26 9.26 17.85 -0.88 8.17 9.35 7.51 10.80 2013 January 11.95 13.33 16.93 8.98 33.64 -11.32 29.93 16.85 5.18 7.34 9.78 23.09 10.01 February 11.47 7.97 15.15 10.14 22.38 -12.87 -2.61 17.29 8.63 13.99 8.30 24.54 10.31 June 2013 | Edition No. 8 March 11.18 11.05 12.64 10.22 23.86 -15.32 -9.49 15.79 4.27 10.96 6.65 23.97 19.56 71 April 10.43 4.16 11.61 7.62 17.47 -12.12 -2.40 13.89 -17.74 16.53 8.23 28.27 15.40 Source: CBK Annexes Annex 16: Money aggregate Growth Rates Broad money Year Money ( M1 ) Money ( M0 ) Reserve Money (yoy) supply ( M2 ) 2011 January 21.5 24.3 18.0 16.1 February 20.5 28.0 17.5 19.7 March 19.4 29.7 18.5 18.0 April 18.2 24.3 19.0 20.1 May 16.6 23.3 17.3 7.9 June 14.5 21.2 17.4 4.8 July 14.7 19.6 19.2 11.5 August 15.2 20.4 19.7 14.8 September 14.3 16.9 18.2 12.5 October 14.0 19.6 16.2 8.1 November 13.8 12.4 16.3 9.5 December 14.1 7.9 11.4 14.5 2012 January 10.6 5.3 13.0 17.2 February 11.2 5.7 12.5 10.4 March 11.5 1.4 13.1 23.2 April 13.0 6.1 8.1 14.7 May 12.5 1.7 10.6 13.2 June 13.1 0.6 6.6 16.7 July 13.9 2.3 3.6 15.6 August 15.0 4.1 5.7 8.4 September 14.3 6.3 5.4 9.7 October 15.8 5.6 3.8 6.7 November 18.1 9.3 7.7 14.0 December 17.2 14.1 7.8 15.1 2013 January 18.2 16.0 11.4 12.2 February 17.0 15.5 17.5 23.9 March 15.7 17.8 15.9 11.5 April 18.5 20.0 13.5 9.5 Source: CBK 72 June 2013 | Edition No. 8 Annexes Annex 17: Mobile payments Number of Number of Value of Number of customers transactions transactions Year Month agents (Millions) (Millions) (Billions) 2011 January 33968 16.7 28.2 75.4 February 34572 16.9 28.5 76.3 March 36198 17.5 32.7 89.0 April 37309 17.8 32.4 86.1 May 38485 17.9 35.3 94.4 June 42840 18.1 35.8 92.6 July 43577 18.3 38.0 99.7 August 44762 18.6 39.3 107.4 September 46234 18.9 39.2 108.6 October 47874 19.2 40.6 109.1 November 49091 19.5 41.2 112.3 December 50471 19.2 41.7 118.1 2012 January 52315 18.8 40.2 114.1 February 53685 18.8 41.8 116.7 March 55726 19.2 45.8 126.1 April 56717 19.5 44.4 117.4 May 59057 19.7 48.0 128.4 June 61313 19.8 47.9 124.0 July 63165 19.6 49.4 129.3 August 64439 19.4 49.7 131.4 September 67301 19.7 48.9 130.7 October 67301 19.7 48.9 130.7 November 70972 20.0 51.9 137.7 December 75226 20.3 53.6 139.0 2013 January 76912 21.1 56.0 150.2 February 85548 21.4 53.4 142.7 March 88393 21.8 53.5 141.1 April 93211 22.3 52.4 134.4 Source: CBK June 2013 | Edition No. 8 73 Annexes Annex 18: Nairobi stock exchange (20 share index) and the dow jones (New York) Year Month 2011 January 4464.9 11,892 February 4240.2 12,226 March 3887.1 12,320 April 4029.2 12,811 May 4078.1 12,570 June 3968.1 12,414 July 3738.5 12,143 August 3465.0 11,614 September 3284.1 10,913 October 3507.3 11,955 November 3155.5 12,046 December 3205.0 12,218 2012 January 3224.9 12,633 February 3303.8 12,952 March 3366.9 13,212 April 3546.7 13,214 May 3650.9 12,393 June 3703.9 12,880 July 3832.4 13,009 August 3865.8 13,091 September 3972.0 13,437 October 4147.3 13,096 November 4083.5 13,026 December 4133.0 13,104 2013 January 4416.6 13,861 February 4518.6 14,054 March 4,861 14,579 April 4,765 14,840 May 5,007 15,116 Source: NSE, and NYSE 74 June 2013 | Edition No. 8 Annexes Annex 19: Nominal and real exchange rate NEER REER Year Month 2003=100 2003=100 2011 January 114 74 February 115 73 March 119 76 April 120 74 May 122 75 June 127 77 July 128 77 August 133 79 September 135 80 October 141 82 November 130 75 December 119 68 2012 January 119 67 February 116 66 March 115 65 April 115 65 May 115 65 June 115 65 July 114 65 August 114 66 September 116 67 October 117 67 November 117 67 December 117.6 66.7 2013 January 118.6 66.4 February 119.0 66.6 March 115.7 64.4 April 113.6 62.8 Source: CBK June 2013 | Edition No. 8 75 76 Annex 20: Fiscal position Year 2004/ 2005/ 2006/ 2007/ 2008/ 2009/ 2010/ 2011/ 2012/ 2005 2006 2007 2008 2009 2010 2011 2012 2013* Revenue and grants 22.7 21.8 22.5 23.3 22.6 25.1 24.6 23.1 29.0 Total Revenue 21.6 20.5 21.6 22.0 21.8 23.9 24.0 22.8 25.3 June 2013 | Edition No. 8 Tax Revenue 19.76 18.66 19.72 20.20 20.37 21.92 21.86 21.05 23.05 Income Tax 7.00 7.17 7.24 7.99 8.24 8.82 9.28 9.28 10.16 VAT 5.65 5.02 5.58 5.70 5.67 5.97 6.17 5.59 6.13 Import Duty 1.75 1.35 1.60 1.68 1.62 1.68 1.65 1.58 1.78 Excise Duty 3.28 3.31 3.27 3.15 3.12 3.04 2.89 2.4 2.43 Other Revenues 2.08 1.81 2.03 1.68 1.72 2.41 1.87 1.96 2.54 Appropriation-in-aid 1.79 1.83 1.92 1.82 1.43 1.93 2.09 1.75 2.25 Grants 1.1 1.3 0.9 1.3 0.8 1.3 0.7 0.5 1.5 Expenditure and Net Lending 22.56 25.20 24.33 27.25 26.62 29.50 29.13 28.89 33.46 Recurrent 19.01 20.18 17.80 20.55 19.46 20.77 21.25 19.72 21.26 Wages and Salaries 7.85 7.39 7.38 7.44 6.94 7.02 7.12 6.84 6.92 Interest Payments 2.27 2.72 2.47 2.44 2.33 2.58 2.73 2.78 2.8 Development and Net lending 3.39 4.46 4.66 6.70 7.16 8.73 7.87 9.16 12.07 Deficit (commitment Basis) Excluding grants -1.01 -4.71 -2.70 -5.23 -4.82 -5.65 -5.18 -6.08 -8.17 Including grants 0.10 -3.39 -1.78 -3.93 -4.01 -4.38 -4.50 -5.62 -6.68 Financing -0.54 2.40 2.10 -0.39 5.23 7.09 4.26 5.23 6.64 Foreign -0.05 0.08 -0.14 0.32 1.84 0.93 1.02 3 3.82 Domestic Borrowing -0.50 2.32 2.24 -0.71 3.39 6.16 3.24 2.23 2.83 Public Debt to GDP (Net) ` 42.6 39.5 42.2 44.9 48.3 44.6 44.6 External Debt 23.3 22.6 24.2 23.2 25.9 23.4 21.7 Domestic Debt 23.5 21.9 23.3 26.9 27.4 26.0 26.0 Source: Ministry of Finance. Quarterly Economic and Budgetary Review,May 2013 *As at the end of March 2013 Annexes Annex 21: 12-Months Cumulative Balance of Payments In millions of US dollars Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013* Annexes 1. Current Account -492 -595 -293 145 -133 -253 -511 -1034 -1973 -1671 -2512 -3330 -4530 -4426 Balance Of Trade -1279 -1273 -828 -636 -1007 -1397 -2226 -2996 -4260 -3892 -4642 -6440 -6893 -6847 2. Merchandise Account -1558 -1614 -1195 -1364 -1906 -2488 -3817 -4936 -6444 -5768 -7169 -9007 -10163 -10367 2.1 Exports (Fob) 1782 1891 2162 2426 2726 3462 3516 4132 5048 4528 5225 5807 6127 6120 Coffee 154 94 84 81 89 128 138 166 155 201 209 222 269 240 Tea 463 435 437 435 456 561 656 693 924 892 1159 1153 1199 1233 Horticulture 209 241 258 351 416 433 509 607 763 692 725 678 695 723 Manufactured Goods 162 175 194 218 292 350 422 513 625 526 608 729 700 675 Other 793 947 1190 1342 1473 1990 1792 2153 2580 2216 2525 3026 3264 3249 2.2 Imports (Cif) 3339 3504 3357 3790 4632 5950 7333 9069 11492 10296 12395 14814 16290 16486 Oil 850 721 607 879 1119 1341 1745 1919 3051 2192 2673 4081 4081 4044 Chemicals 431 479 508 591 746 833 1004 1156 1446 1324 1603 1947 2076 2066 Manufactured Goods 361 437 421 497 687 779 1065 1435 1589 1411 1774 2250 2302 2430 Machinery & Transport Equip- 944 1162 1060 969 1119 1783 2252 2800 3063 3065 3808 3686 4748 4957 ment Other 753 705 760 854 961 1214 1267 1759 2343 2304 2537 2848 3083 2990 3. Services 1065 1019 902 1509 1773 2234 3306 3902 4470 4097 4657 5676 5633 5941 3.1 Non-Factor Services 279 341 367 728 898 1091 1591 1940 2184 1876 2527 2566 3270 3520 3.2 Income Account -133 -123 -143 -88 -127 -109 -70 -143 -45 -38 -158 7 -141 -165 3.3 Current Transfers Account 920 801 678 869 1001 1253 1785 2106 2331 2259 2288 3103 2504 2586 of Which Remittances 382 408 574 611 609 642 891 1170 1180 4. Capital & Financial Account 710 967 351 219 250 560 1186 1888 1505 2451 2675 3288 5757 5068 4.1 Capital Account 63 69 81 163 145 188 211 267 294 290 154 235 155 148 4.2 Financial Account 647 898 270 56 105 372 975 1621 1210 2161 2522 3053 5601 4920 4.2.1.1 Official, Medium- & Long-Term -170 -284 -44 -229 -195 -216 -202 -16 106 466 308 4.2.1.2 Private, Medium- & 96 307 257 84 -20 458 38 592 72 44 176 35 -73 -103 Long-Term 4.2.1.2.3 Direct 143 -18 -42 55 -7 -55 -11 438 153 127 106 107 109 110 Investment (Fdi) June 2013 | Edition No. 8 4.2.1.3 Commercial Banks (Net) -221 95 -172 104 -122 -202 -156 -5 15 494 61 -213 873 79 77 78 June 2013 | Edition No. 8 Annex 21: 12-Months Cumulative Balance of Payments In millions of US dollars Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013* 4.2.2 Short Term And Net Er- 942 780 229 97 442 332 1296 1050 1017 1158 1977 2891 3654 3699 rors & Omissions (Neo) Short Term (Incl. Portfolio 379 396 348 423 443 568 714 1032 995 577 1130 1678 2454 2531 Flows) Net Errors And Omissions 562 384 -119 -326 -1 -236 582 18 22 581 847 1213 1200 1168 (Neo) 5. Overall Balance 217 372 59 365 117 306 675 854 -469 781 163 -43 1227 642 Memo: Gross Reserves 1398 1459 1614 1887 2078 2534 3331 4557 4641 5064 5123 6045 7160 7114 Official 897 1064 1067 1480 1519 1799 2415 3355 2875 3847 4002 4248 5702 5523 Commercial Banks 501 395 547 408 560 735 916 1202 1765 1217 1121 1797 1458 1591 Imports Cover (Calender Year) 2.7 3.0 3.2 4.2 3.4 3.2 3.55 4.00 2.75 4.08 3.55 3.12 3.87 3.69 Import Cover (36 Mths Im- 3.3 4.4 4.1 3.98 3.89 4.84 3.36 4.08 3.85 3.71 4.31 4.06 ports) Source: CBK * cumulative 12 months to April 2013 Annexes Annexes Annex 22: Growth Outlook 2012 2013* 2014* 2015* BASELINE GDP 4.6 5.7 5.9 5.5 Private Consumption 2.7 2.9 3.1 2.8 Government Consumption 8.0 4.6 3.7 3.0 Gross Fixed Investment 20.5 12.1 15.0 13.2 Exports, GNFS 5.0 5.4 6.4 6.7 Imports, GNFS 13.0 5.8 8.0 7.7 Output Gap (Percent of Potential GDP) -0.2 0.6 1.3 1.8 HIGH CASE SCENARIO GDP 4.6 6.1 6.7 6.5 Private Consumption 2.7 3.1 3.1 3.1 Government Consumption 8.0 4.6 3.7 3.0 Gross Fixed Investment 20.5 15.0 19.0 18.0 Exports, GNFS 5.0 5.4 6.4 6.7 Imports, GNFS 13.0 7.0 9.0 9.5 Output Gap (Percent of Potential GDP) -0.2 0.8 2.1 3.1 LOW CASE SCENARIO GDP 4.6 4.4 4.4 4.6 Private Consumption 2.7 2.6 2.6 2.7 Government Consumption 8.0 4.6 3.7 3.0 Gross Fixed Investment 20.5 Exports, GNFS 5.0 5.4 6.4 6.7 Imports, GNFS 13.0 5.0 6.0 6.5 Output Gap (Percent of Potential GDP) -0.2 -0.5 -0.7 -0.5 Source: World Bank Computation June 2013 | Edition No. 8 79 Annexes Annex 23: Maize prices in Kenya Maize is very important in daily Kenyan meal offers an advantage of getting a better estimate with a per capita consumption level of 77.2 kg of monthly average; and also reduces the per year1 and at the same time taking a large probability of having data gaps. For comparison, proportion of lower income groups’expenditure. prices from all sources were normalized to Increased maize prices would be therefore US dollar per metric ton and weighted with transmitted to food inflation; affect food security counties population shares to reach an overall and therefore leading to escalated poverty levels average. A three months moving average was and widened income inequalities. Kenya’s prices used to overcome the problem of missing for maize have been trending above global maize values. All data sources displayed high price in prices, triggered by several factors including the July which shrank in September before picking hike of global food prices, and more importantly up again in January. This trend is attributed to bad climatic conditions that affected domestic maize planting and harvesting seasons. production which accounts for74 per cent of maize domestic supply.2 However a major To some extent these inconsistencies arise as challenge would be how high these prices are. a result of different methodologies used in data collection and distribution costs across Existing data sources showed irregularities in markets. FAO and M-Farm maize pricesboth maize price levels, despite the fact that they all being at a wholesale level are close at an average had the same trends. KNBS, FAO and M-Farm deviation of US$ 17 per MT for the period maize prices data are compared in Figure 1 below. since January 2011. However, data gaps are KNBS prices are monthly retail prices collectedin wide between M-Farm and KNBS. The average different markets across the country. FAO and deviation of M-Farm from KNBS price levels was M-Farm’s; which are both non-governmental US$ 80 per MT in Kisumu, US$ 87 in Mombasa, bodies; are at a wholesale basis from major US$ 103 in Nairobi and US$ 118 in Eldoret for the towns of the country including Nakuru, Busia, same period. Retail prices of KNBS may reflect Nairobi, Mombasa, Kisumu, Eldoret and Kitale. distribution cost, profits, and the gap became Contrarily to other sources, M-Farm daily data much higher in 2012 compared to the year 2011. There are wide maize price disparities across data sources, and all lie above global maize prices. 700 700 Average maize price in US$ per MT 600 600 US dollar per metric ton 500 US$ 509 500 US$ 509 400 US$ 380 400 US$380 300 US$ 302 300 US$303 200 200 100 100 0 0 FAO M-Farm KNBS M-Farm KNBS WB Source: World Bank staff computation based on KNBS, M-Farm, FAO and GEM data. 1 FAO Statistics (values for the year 2009) from www.fao.org 2 According to FAO in 2009: total production was 2.4 million MT while supply was 3.2 million MT. 80 June 2013 | Edition No. 8 Annexes … with data inconsistencies at a lower level in Kisumu Eldoret Kisumu 800 800 600 600 US$ per MT US$ per MT 400 400 200 200 0 0 Jan-11 Mar-11 Mar-12 May-12 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 Jan-11 Mar-12 May-12 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Jul-12 Nov-12 Jan-13 Mar-13 Sep-12 FAO $/MT Mfarm $/MT KNBS $/MT FAO $/MT Mfarm $/MT KNBS $/MT Mombasa 800 Nairobi 600 600 US$ per MT 400 US$ per MT 400 200 200 0 0 Jan-11 Mar-11 Jul-11 Nov-11 Jan-12 Mar-12 May-12 Jan-11 Mar-11 May-11 Sep-11 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 FAO $/MT Mfarm $/MT KNBS $/MT FAO $/MT Mfarm $/MT KNBS $/MT Source: Computation based on KNBS, M-Farm and FAO data For the three sources of data, maize is relatively Domestic maize prices also increased over time cheaper in Eldoret and Kisumu which are among due to Kenya National Cereal and Produce Board the largest maize surplus markets compared to (NCPB) inefficiency in its intervention in maize Mombasa and Nairobi. market to fix prices for both sale and purchases. Government initiative through NCPB to promote Whereas quality data plays a vital role in offering free and fair trade in commodities and their accurate information which may further be timely accessibility putpressure on maize prices used for analysis and forecasting; where data both at wholesale and retail levels. Not only is wrong or does not exist at all, it would be have NCPB purchase prices been above those in hard for policy makers not only to provide and other domestic markets but also its sellers have predict good development paths but also to been mainly large-scale farmers.3 evaluate their existing policies’ implications. 3 Ariga, J et al, 2010. Staple Food Policies in Kenya, a Paper Prepared for the COMESA policy seminar on Variation in Staple Food Prices: Causes, Consequence, and Policy Options. The Comesa-MSU-IFPRI African Agricultural Marketing Project (AAMP). Maputo, Mozambique, 25-26 January 2010. June 2013 | Edition No. 8 81 Annexes Annex 24: Methods National Accounts based predictions of poverty The base projection calculates per adult equivalent consumption recursively: The general approach models the trajectory of Forward projection Backward projection consumption per capita obtained from household from 2005 to 2011: from 2005 to 1991: surveys using observed growth rates in GDP per capita from national accounts (Datt, Ramadas, Mensbrugghe, Walker, & Wodon, 2002). The model can accommodate different GDP and The survey weights are adjusted similarly to population growth rates for specific regions and reflect year by year population changes: economic sectors and different scenarios of how the distribution of consumption (inequality) changes over time. In this application, per adult Forward projection Backward projection equivalent household consumption is projected from 2005 to 2011: from 2005 to 1991: both forwards and backwards from the 2005 KIHBS to produce yearly poverty estimates (using the 2005 absolute poverty line) between 1991 and 2011 (Demombynes & Hoogeveen, 2007). The assumption of distribution-neutral growth The model relies on data from three sources: can be relaxed by adjusting consumption for household consumption from KIHBS, GDP data each household within each sector year-by-year: from national accounts and employment data from the census (Table below). Consumption model parameters Model Parameters Detail Data Source and Notes Per adult equivalent consumption for 2005/06 KIHBS. samplehousehold in year . Individual weight for household in 2005/06 KIHBS. year . Sector of household in survey year 2005/06 KIHBS, based on primary occupation (2005) of household head. Real GDP growth rate for sector of WDI/Kenya National Accounts. household in year . Employment growth rate for sector of World Development Indicators: household in year . To obtain sector-specific population growth rates, the overall employment: population ratio was used in combination with the share of total employment by sector of occupation obtained from the Census and 2005/06 KIHBS. The average growth rate between known data points was used. Gini coefficient growth rate in sector in year . 82 June 2013 | Edition No. 8 Annexes ′ This adjustment is applied after the base where 𝑥𝑥��ℎ𝑡𝑡 are the �� consumption predictors projection is completed. If inequality has that are available at the household level both at increased, this procedure redistributes time 𝑡𝑡 and 𝑡𝑡 ൅ 𝑛𝑛𝑜𝑜𝑟𝑟𝑡𝑡 − 𝑛𝑛, 𝛽𝛽𝑡𝑡 is a vector of �� consumption from households with parameters and 𝑢𝑢��ℎ𝑡𝑡 is a heteroskedastic error consumption levels below the average for their term that is made up of a cluster component sector of employment to households above the ሺ𝑛𝑛��𝑡𝑡 ሻ and a household component ሺ𝜖𝜖��ℎ𝑡𝑡 ሻ as average for their sector of employment. Note follows 𝑢𝑢��ℎ𝑡𝑡 ൌ 𝑛𝑛��𝑡𝑡 ൅  𝜖𝜖��ℎ𝑡𝑡 . To obtain the that in a scenario where growth is distribution variance-covariance matrix of (1) a GLS neutral, the change in the Gini coefficient is regression model is used (as the error term is zero and no adjustment is made to not independent and identically distributed). consumption. The core assumption of this The procedure estimates both the variation of model is the correspondence between growth 𝑛𝑛��𝑡𝑡 (which captures the correlation between in consumption per capita measured in the consumption in groups of households that are household survey and growth in income per spatially proximate) and the variance of𝜖𝜖��ℎ𝑡𝑡 . capita measured through national accounts. ′ Since at time 𝑡𝑡 ൅ 𝑛𝑛 only 𝑥𝑥��ℎ ǡ𝑡𝑡 ൅𝑛𝑛 is observed and Household asset based predictions of poverty not consumption ����ℎ ǡ𝑡𝑡 ൅𝑛𝑛 , the error term 𝑢𝑢��ℎ ǡ𝑡𝑡 ൅𝑛𝑛 is also unknown and the expected value Only a brief exposition of the procedure is of poverty 𝑃𝑃𝑡𝑡 ൅𝑛𝑛 is estimated given the outlined here as comprehensive and detailed ′ observed 𝑥𝑥��ℎ ǡ𝑡𝑡 ൅𝑛𝑛 and the estimated model explanations are provided elsewhere ′ parameters of (1) so that 𝐸𝐸 ሾ𝑃𝑃𝑡𝑡 ൅𝑛𝑛 ሺ𝑥𝑥��ℎ ǡ𝑡𝑡 ൅𝑛𝑛 , 𝛽𝛽𝑡𝑡 ൅𝑛𝑛 , (Christiaensen, Lanjouw, Luoto, & Stifel, 2011; Elbers, Lanjouw, & Lanjouw, 2002). The poverty 𝑢𝑢��ℎ ǡ𝑡𝑡 ൅𝑛𝑛 ሻሿ. This expectation is computed through head count estimate (or any other welfare simulation by taking draws from the estimated measure based on consumption) is defined as a distributions of 𝛽𝛽𝑡𝑡 and 𝑢𝑢��ℎ𝑡𝑡 Ǥ Note the core function of consumption ����ℎ𝑡𝑡 (for household ℎ, assumption is that 𝛽𝛽𝑡𝑡 ൅𝑛𝑛 = 𝛽𝛽𝑡𝑡 (the distributions in cluster �� at time 𝑡𝑡) (in this case 𝑡𝑡 = 2005) of 𝛽𝛽𝑡𝑡 remain constant over time) and the as𝑃𝑃𝑡𝑡 ሺ����ℎ𝑡𝑡 ሻ. To obtain a definition of relationship determining the hetorskedastic consumption that can be applied to predict nature of the data generating process is also consumption at any future 𝑡𝑡 ൅ 𝑛𝑛 or past assumed to be constant. 𝑡𝑡 − 𝑛𝑛 point in time, an estimator of log-linear The adapted SAE technique relies on the strong consumption at time t is defined as follows: assumption that the parameter estimates that ′ 𝑙𝑙𝑛𝑛����ℎ𝑡𝑡 ൌ 𝑥𝑥��ℎ𝑡𝑡 𝛽𝛽𝑡𝑡 ൅ 𝑢𝑢��ℎ𝑡𝑡 define consumption are stable over time. June 2013 | Edition No. 8 83 Annexes Annex 25: Illustrations of distributional impact of inequality on the distribution of consumption The effect of growth and 3 different scenarios of inequality on The effect of growth and 3 different scenarios of inequality on the distribution of consumption, 2005 – 2011, Rural Kenya the distribution of consumption, 2005 – 2011, Urban Kenya Distribution neutral growth Distribution neutral growth Urban poverty line 2005 2005 2011 2011 Rural poverty line Monthly expenditure per A.E. (2005 Kshs) 0 2000 4000 6000 8000 10000 Monthly expenditure per A.E. (2005 KES) 0 2000 4000 6000 8000 10000 Growth with decreasing inequality: Growth with decreasing inequality: -3 percent per year -3 percent per year Urban poverty line 2005 2005 2011 2011 Rural poverty line Monthly expenditure per A.E. (2005 Kshs) 0 2000 4000 6000 8000 10000 0 2000 4000 6000 8000 10000 Monthly expenditure per A.E. (2005 KES) Growth with increasing inequality: +3 percent per year Growth with increasing inequality: +3 percent per year Urban poverty line 2005 2005 2011 2011 Rural poverty line Monthly expenditure per A.E. (2005 Kshs) 0 2000 4000 6000 8000 10000 0 2000 4000 6000 8000 10000 Monthly expenditure per A.E. (2005 KES) Source: World Bank Source: World Bank 84 June 2013 | Edition No. 8 Annexes Annex 26. National accounts based predictions of poverty using $1.25 dollar per day poverty line National accounts based projections of headcount poverty in Kenya, 1990- 2011 All Kenya 60 Poverty Headcount (percent) 50 46 46 43 44 40 41 36 Inequality Scenarios Long term change in gini: 32 30 +2% 28 +1% 0% 20 -1% -2% 2005 KIHBS poverty rate 10 1990 93 96 99 02 05 08 2011 70 Rural 60 54 Poverty Headcount (percent) 53 51 52 50 49 44 40 39 35 30 20 10 1990 93 96 99 02 05 08 2011 50 Urban 40 Poverty Headcount (percent) 30 20 14 14 13 11 11 10 7 4 2 0 1990 93 96 99 02 05 08 2011 Source: World Bank June 2013 | Edition No. 8 85 86 Annex 27. Trends in household living standard indicators between 1989 & 2009 Figure A.1 Average trajectory of indicators ofhousehold living standards, 1989-2009 June 2013 | Edition No. 8 Kenya Nairobi Other Urban Rural 100 100 100 100 Ownership of consumer durables Ownership of consumer durables Ownership of consumer durables Ownership of consumer durables Asset: Annual Growth (%) Asset: Annual Growth (%) Asset: Annual Growth (%) Asset: Annual Growth (%) Television: +9.1* Television: +6.2* Television: +4.5* Television: +12* Mobile Phone: +31.7* Mobile Phone: +13.6* Mobile Phone: +25.1* Mobile Phone: +38.7* 80 80 80 80 Computer: +32* Computer: +17.3* Computer: +37.4* Computer: +37.6* Radio: +1.8* Radio: .7 Radio: .2 Radio: +2.2* Fridge: +3.9* Fridge: 3.3 Fridge: -.2 Fridge: 3.3 60 Vehicle: .5 60 Vehicle: -.5 60 Vehicle: -1.1 60 Vehicle: 0 Motorbike: 8.9 Motorbike: -3.2 Motorbike: 7.5 Motorbike: +11.8* Bike: +1.4* Bike: -.7 Bike: .9 Bike: +1.9* 40 * = statistically signi�cant 40 * = statistically signi�cant 40 * = statistically signi�cant 40 * = statistically signi�cant Percent of households Percent of households Percent of households Percent of households 20 20 20 20 0 0 0 0 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 Kenya Nairobi Other Urban Rural 100 100 100 100 Source of Water Source of Water Asset: Annual Growth (%) Asset: Annual Growth (%) Piped or Public tap: -.1 Piped or Public tap: .1 80 Well: +2* 80 80 80 Well: +1.2* Other (Surface Water, Spring, Lake): -1.1* Other (Surface Water, Spring, Lake): -.7 * = statistically signi�cant * = statistically signi�cant Source of Water Source of Water 60 60 Asset: Annual Growth (%) 60 Asset: Annual Growth (%) 60 Piped or Public tap: -.6 Piped or Public tap: -2.3* Well: +13.8* Well: +7.6* 40 40 Other (Surface Water, Spring, Lake): -4.3 40 Other (Surface Water, Spring, Lake): 1.5 40 * = statistically signi�cant * = statistically signi�cant Percent of households Percent of households Percent of households Percent of households 20 20 20 20 0 0 0 0 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 Annexes Kenya Nairobi Other Urban Rural 100 100 100 100 Waste facility Asset: Annual Growth (%) Flush toilet: 1.1 Pit Latrine: -2.1 80 80 80 80 Other (Bucket, No Facility): -6.6 * = statistically signi�cant Annexes Waste facility Waste facility Waste facility 60 Asset: Annual Growth (%) 60 60 Asset: Annual Growth (%) 60 Asset: Annual Growth (%) Flush toilet: +1.2* Flush toilet: -1.4 Flush toilet: -2.4 Pit Latrine: +.3* Pit Latrine: .7 Pit Latrine: +.4* Other (Bucket, No Facility): -2* Other (Bucket, No Facility): -4.1 Other (Bucket, No Facility): -1.3* 40 40 40 40 * = statistically signi�cant * = statistically signi�cant * = statistically signi�cant Percent of households Percent of households Percent of households Percent of households 20 20 20 20 0 0 0 0 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 Kenya Nairobi Other Urban Rural 100 100 100 100 Housing Infrastructure Housing Infrastructure Asset: Annual Growth (%) Asset: Annual Growth (%) Roof: Tiles or concrete: +2.5* Roof: Tiles or concrete: -.8 Floor: Wood or cement: +2.2* Floor: Wood or cement: +2.2* 80 80 80 80 Access to Electricity: +4.6* Access to Electricity: +5* * = statistically significant * = statistically significant [% of households] [% of households] [% of households] [% of households] Housing Infrastructure 60 60 60 Asset: Annual Growth (%) 60 Roof: Tiles or concrete: -.1 Floor: Wood or cement: .4 Access to Electricity: +2.5* 40 40 40 40 * = statistically significant Housing Infrastructure Asset: Annual Growth (%) 20 20 Roof: Tiles or concrete: +2.5* 20 20 Floor: Wood or cement: +1.1* Access to Electricity: +3.1* * = statistically significant 0 0 0 0 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 Kenya Nairobi Other Urban Rural 100 100 100 100 Education of head of household Education of head of household Education of head of household Education of head of household Asset: Annual Growth (%) Asset: Annual Growth (%) Asset: Annual Growth (%) Asset: Annual Growth (%) No formal education: -3.6* No formal education: -6.2* No formal education: -2.6* No formal education: -3.2* Some primary: .3 Some primary: -3.7* Some primary: -.3 Some primary: .9 80 80 80 80 Primary complete: 1.3 Primary complete: -.9 Primary complete: .6 Primary complete: 1.7 Some Secondary: -3 Some Secondary: -6.7* Some Secondary: -5.2* Some Secondary: -2 [% of households] [% of households] [% of households] [% of households] Secondary complete: 6.8 Secondary complete: 5.8 Secondary complete: 3.8 Secondary complete: 7.7 60 Post-secondary: +11* 60 Post-secondary: +8.8* 60 Post-secondary: +9.1* 60 Post-secondary: +12* * = statistically significant * = statistically significant * = statistically significant * = statistically significant 40 40 40 40 20 20 20 20 June 2013 | Edition No. 8 0 0 0 0 87 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 89 93 97 01 05 09 Source: World Bank Time to shift gears Accelerating growth and poverty reduction in the new Kenya Kenya entered 2013 on a strong economic footing, and after peaceful elections and transition, growth is projected to rise to 5.7 percent at the end of the year, and 6 percent in 2014, supported by lower interest rates and higher investment growth. This report focuses on poverty reduction in the new Kenya, citing the progress made since 2005, when an estimated 47 percent of the population lived below the poverty line, to the present, where poverty estimates range between 34 and 42 percent, the imprecision resulting from the lack of any recent survey data. The report notes the spatial dimension of poverty, and the poor tend in the arid and semi-arid regions in the north and north east. It concludes with thoughts about a poverty reduction strategy, which would emphasize on job creation, enhanced productivity of smallholder farms, strengthening and expanding cash transfer programs, targeted public spending programs to provide quality education to the rural poor, and improved poverty monitoring, so that the government can rapidly see which activities have the greatest impacts on improving the lives of the poor. Join the conversation! WHAT CAN GOVERNMENT DO TO REDUCE POVERTY IN KENYA? Text message your answers to: +254 700 186 473 Tweet your answers using the following hashtag in your response: #tumalizeumaskini Produced by Poverty Reduction and Economic Management Unit Africa Region. Photo credits: Original Images – www.originalimages.co.ke Design by Robert Waiharo.